For a few bucks you could get a website with some name like MyGeneHistory dot com to send you a little swab stick, which you would swizz around your gums and send back to them. They would, in turn, do some DNA sequencing on it, and purport to tell you all sorts of exciting things about your ancestors, or your disease risk, or your most suitable diet. There were loads, of which the most famous was probably 23andMe, which recently filed for bankruptcy protection.

One of the funnier ones, the now extinct BritainsDNA, would tell customers stuff about how their ancestors were Germanic tribespeople marauding across Europe and terrorizing Romanized town-dwellers in the first millennium, or that they “arrived in the British Isles and Ireland around 3,000 BC”, or brought porridge (seriously) to Britain some time before the birth of Christ.

Obviously that’s all silly, because — bear with me on the math here — you had two parents, four grandparents, and so on. Assuming 30-year generations (probably too long, but let’s be conservative), 1,000 years ago you would have had around 800 million great^33 grandparents. Since the world population was probably about 300 million at the time, that’s unlikely.

What it actually means is that your ancestors interbred. And that, in turn, means that everyone who was alive in Europe about a thousand years ago was either the ancestor of every European person alive or of no one. If you are a white European, your ancestors, past a certain point, are the same as every other white European’s ancestors.

The science of BritainsDNA wasn’t completely made up. The technology they used — looking at mitochondrial and Y-chromosome DNA, passed down through the female and male lines respectively — really has led to some important scientific breakthroughs, particularly in confirming that humans came out of Africa about 200,000 years ago. But it couldn’t tell you anything very interesting about your ancestors specifically. It made its money (briefly: it shut down in 2017) by taking the real and interesting population-level science and claiming it could say meaningful things about individuals.

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This is a pretty standard trick — vitamin companies have done it for years. They note that some mineral or chemical is important in some bodily process, like saying vitamin B helps your body process energy — which is true — and then use that to imply that taking their vitamin B supplements will boost your energy, when in most cases your body has all the vitamin B it needs and will just piss out any extra that you give it. Commercial genetics companies, often, simply follow the same playbook.

23andMe promised a future of precision-guided, individualized medicine, in which a simple genetic test would tell you your risk of various diseases. It was not as overtly silly in its claims as BritainsDNA, but even early on it had problems. In 2013 the US Food and Drug Administration said its scientific claims were unevidenced and ordered it to take down its health test products: it relaunched them two years later for a more limited range of diseases, but it still faced two key issues.

One was that the last two decades have seen the great hopes of personalized genetic medicine not fade away exactly but become more complicated. The framework that dominated genetic research was called the “candidate gene” model: scientists looked for “the gene for” diabetes, or “the gene for” obesity, or schizophrenia, or alcoholism, or heart disease.

But the rise of genome-wide association studies — research which looks at the entirety of human DNA and uses big data techniques to see the correlation between certain genes and phenotypic outcomes — revealed candidate genes, “genes for” things, to be vanishingly rare. They do exist — Huntington’s disease and cystic fibrosis are caused by single-gene mutations — but most health conditions are caused by the interaction of hundreds or even thousands of genes spread across the genome, each of which has only a tiny effect on your risk of depression or gout or whatever. It is not especially useful to know that you have a 3.8% lifetime risk of Parkinson’s rather than 2.7%.

And the other problem is that often the information is of limited use. If you learn you have a raised risk of high blood pressure or melanoma, the advice will be the same as for everyone else: take exercise, wear sunscreen. 23andMe says it could lead to earlier screening and detection, and perhaps there’s something in that, but cancer screening is itself of ambiguous benefit. (To be fair, it is of more use if applied to higher-risk populations.) I don’t think it’s too harsh to say that a lot of the time, these “polygenic risk scores” of diseases will simply scare people by telling them of largely irrelevant increases in their risk which they can do very little about.

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It’s easy, perhaps, for me to say that these genetic medicine companies were overhyped. Perhaps a more interesting exercise is predicting which current healthcare fad won’t survive scrutiny.

Diet is always a safe bet. At least one of the commercial genetics companies has had a go at it: DnaNudge, which I wrote about back in 2020, tests customers’ DNA and claims to tell you which foods you should avoid. A geneticist at the time pointed out to me that this was close to being “snake oil”. Again, the genetic variants involved might tell you some statistically significant population-level thing, like 125 out of every 100,000 people with a particular gene might have wheat intolerance, instead of 103 in the background population. But it’s of very little use to individuals. (That company got given $161 million by the government for COVID testing, incidentally.)

These days people are very excited about the ZOE app, which again came to prominence in the pandemic. Users’ blood glucose is continuously monitored by a personalized health widget as they eat different foods, and — as Deborah Cohen and Margaret McCartney wrote in UnHerd two years ago — they also have to poo into a “hammock” over the loo bowl and post the resulting sample off for testing to tell you about your “gut microbiome”, the collection of bacteria and other organisms in your intestine. Then ZOE provides a personalized nutritional program of foods to eat or avoid, which it says can help you lose weight, sleep better, avoid health risks, and so on.

But again this is pretty suspect. For one thing, while there are some extraordinary claims about the gut microbiome and its links to various health conditions — people have said it causes Alzheimer’s, autism, depression, and a hundred other things — the evidence is very thin. The long and short of it is that the research is plagued by small sample sizes, bad statistical techniques, and overextrapolation from tiny animal studies. It seems pretty unlikely that you can give an individual any useful information.

And as Cohen and McCartney pointed out in 2023, while continuous glucose monitoring is important in diabetics whose bodies can’t control their blood sugar levels, healthy people can, and it’s not clear that a short-term glucose spike brought on by eating a banana is particularly informative. And the same food seemed to have different effects in the same person at different times, or even when monitored by two different devices at the same time, suggesting that the tests are unreliable.

So how about longevity? Anti-aging science is undoubtedly promising. There are hints that a particular chemical change to one of the four “letters” of our DNA tracks, and possibly influences, our biological age; and there are encouraging therapies which could reverse that chemical change and which, just possibly, could slow or stop aging. Other interesting avenues of research include telomeres, the tips of our chromosomes, which grow shorter as we age, allowing the chromosomes to fray. Reversing that process could influence aging. There are strong hints that severe calorie restriction can extend life in mice.

But the science here is uncertain and complicated. Surely no one would try to sell it yet? Well, obviously they do. The tech entrepreneur Bryan Johnson has for the last several years has been on an extreme anti-aging regime called Project Blueprint: he claims now to be “the healthiest person alive” (“If health were an Olympic sport, I’d be the gold medalist”). And that’s great! I hope it works out for him and that he lives a long time. I also hope that longevity science improves, because aging has many very unpleasant side effects (ask my knees) and spending more time on the planet with your loved ones is good.

What Johnson also does, though, is sell “Longevity Mix” supplements on his website for $55 a go, or you could treat yourself to the “Blueprint Metabolic Stack”, which “streamlines your wellness journey with a simple format comprising a supplement drink, protein meal, seven daily pills, and premium Extra Virgin Olive Oil”. A mere $386 for a month’s supply, and it comes with a free hoodie!

Johnson is not alone. As The Wall Street Journal noted recently, anti-aging is big business. Not just anti-wrinkle face creams and things, although they’re silly enough, I suspect: actual medicines which purport to slow bodily aging. One scientist locates the aging process in a family of proteins called sirtuins, which are involved in metabolism, DNA repair, inflammation, and the function of your metabolism and mitochondria. There was some evidence that boosting the expression of the gene that creates sirtuins can indeed extend life — in yeast, and possibly worms and flies. But the pharma industry analyst Derek Lowe argued that even that research is hopeless, “a steaming pile of artifactual results, overinterpretation, and publication bias”. And yet you can buy sirtuin supplements for $720 for an annual subscription.

My fairly confident prediction is that people who buy these supplements will not live significantly longer, on average, than other, similarly health-conscious members of their social class. Come back and ask me in 60 years.

23andMe was a product of its time, a period in which gene sequencing was the exciting science of the moment. But it was also somehow timeless: people have always sought to turn cutting-edge science into snake oil, at least as far back as 1918, when the discovery of radiation led to radium-infused “toothpaste, hair cream, cosmetics and even suppositories”. This overhyping of science and selling it for a monthly subscription fee is a business model destined to outlive even Bryan Johnson.

A paediatrician, Roy Meadows, called as an expert witness for the prosecution, told the court that the probability of the two deaths happening by chance was one in 73 million: that is, 8,500 times 8,500.

As it happens, that’s not true. This calculation assumes that the deaths are entirely uncorrelated, but we know that SIDS can run in families and be affected by environmental conditions. If you have one case of SIDS in your household, while incredibly rare, you are more likely to have a second; the 73 million figure is orders of magnitude too high. But that wasn’t Meadows’s big mistake.

His big mistake was the following: he assumed that if the probability of the two deaths happening by chance was one in 73 million, then the probability that Sally Clark was innocent was one in 73 million as well.

But this is wrong. Crucially, catastrophically wrong. As wrong as assuming that because only one human in eight billion is the President of the United States, there’s only a one-in-eight-billion chance that the President of the United States is human.

Nonetheless, Meadows’s testimony helped convict Clark in 1999. She spent three years in jail before her conviction was overturned on appeal. Her life was, obviously, ruined. It will not surprise you to learn that she drank herself to death four years later, alone. It’s a haunting story.

The mistake made in Clark’s case is a subcategory of a far wider failure of reasoning, a failure I discuss in my new book, Everything is Predictable: How Bayes’ Remarkable Theorem Explains the World. But in legal circles it comes up again and again — often enough to have its own name: the “prosecutor’s fallacy”.

There was a recent article in the New Yorker about the nurse Lucy Letby, convicted of murdering seven babies in a neonatal ward. The online version is blocked in the UK, because of contempt-of-court laws, despite her appeal against the convictions being denied: she still faces a retrial on one count of attempted murder.

This piece is not about Letby. I do not know the facts of the Letby case and would not be allowed to write about them if I did; whether that is a strength or a weakness of British law, I leave to others to discuss. But I do know that courts, in the UK, US and elsewhere, have a bad history when it comes to the use of statistical evidence. To understand why, we need to go back to the work of an 18th-century nonconformist minister.

The Reverend Thomas Bayes’s eponymous theorem was published in a paper called “An Essay towards solving a Problem in the Doctrine of Chances” in 1763, five years after his death. Previous work in probability theory had answered the question: how likely am I to see some event, given a hypothesis? For instance, if we assume that my dice are fair, and I roll three of them, I can expect to see three sixes one time in 216. That’s called sampling probability.

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But most of the time, with statistics, we want to answer the opposite question: how likely is my hypothesis to be true, given some new event? That’s called inferential probability, and it’s a completely different thing.

Say I go to the doctor’s and I get a cancer test. It’s quite an accurate test: if I have cancer, it will correctly say so 99 times out 100; if I don’t have cancer, it will correctly say so 99 times out of 100. If I get a positive result, then, what’s the chance that I have cancer? Is it 99%?

No. The answer is you don’t know. At least, not with the information I’ve given you.

Imagine that this particular cancer is rare: only one person in 1,000 has it. You test 100,000 people at random. Of that 100,000, about 100 will have the cancer, and your test will pick up 99 of them. Of the remaining 99,900, your test will correctly say that 98,901 are cancer-free.

But that means that it will incorrectly say that 999 people do have cancer when they don’t. So of your 100,000 tests, 1,098 will come back positive, and only 99 of them are true positives. If you are one of them, then there is just a 9% chance you have cancer.

You can’t answer the question “How likely am I to have cancer, given this test?” without first answering the question “How likely did I think I was to have cancer in the first place?” That was Bayes’s big insight. You need what is called a prior probability. If the cancer was less rare, then your positive test would be more worrying: if one person in every 100 had it, then a positive result would mean about a 50% chance you have the disease.

It’s counterintuitive and weird. What do you mean, this 99% accurate test result is almost certainly wrong? But it is mathematically unavoidable.

You can probably see the bearing this has on statistical evidence used in court. Take DNA tests, for instance: you might do a DNA test on a sample from a crime scene. It matches a result on your database. There’s only a one in 3 million chance that someone’s DNA would match the sample by chance. Does that mean there’s only a one-in-3-million chance that your suspect is innocent? As you’ll realise by now, no it does not.

It depends on your prior probability. If your database is a random sample of the British population, then the prior probability that any given person is the culprit is one in 65 million. If you tested the whole population, you’d get about 20 matches just by chance.

But if you are a modern-day Hercule Poirot, and you’re only testing 10 people trapped by a snowstorm in a country mansion, then your prior probability is one in 10, and the chance it’s a false positive is about one in 300,000.

Real cases have turned on these details: a man called Andrew Deen was convicted of rape in 1990 on the basis of DNA evidence that an expert witness said there was only a one in 3 million probability of a chance match. His conviction was overturned — although he was found guilty again in a retrial — because a statistician pointed out that “How likely is it that a person’s DNA would match the sample, if they are innocent?” and “How likely is it that someone is innocent, given that their DNA matches the sample?” are very different questions.

In Sally Clark’s case, the problem was not testing, but clustering: two rare events happening simultaneously. But, again, the probability of seeing those events by chance is not the same as the probability that she was guilty.

Hers is far from the only case in which the use of statistics has raised suspicion. In 2022, the Royal Statistical Society wrote a report on statistical evidence in criminal trials, and noted that one of the most common reasons that medical professionals are accused of murders is because “an apparently unusual number of deaths occurs among their patients”.

But these cases are doubly difficult to evaluate, the report noted, because “they involve at least two levels of uncertainty”. As well as the normal uncertainty over whether an individual committed a murder, there is uncertainty over whether any murders occurred at all.

The Dutch paediatric nurse Lucia de Berk was convicted, in two trials in 2003 and 2004, of seven murders and three attempted murders of children under her care. A criminologist told her trial that “the probability of so many deaths occurring while de Berk was on duty was only one in 342 million”.

But even if that were the case — and again, it wasn’t; the RSS estimated that if you took into account all the relevant factors, the chance of seeing a cluster like that could be as high as one in 25 — that’s not the same as the chance that de Berk was innocent. In order to establish that, you would need to take into account the prior probability that someone would be a multiple murderer — a mercifully tiny chance. De Berk’s conviction was overturned in 2010, thanks in part to the work of Bayes-savvy statisticians.

Bayesian reasoning doesn’t only reveal the wrongly convicted — in some instances it could have led to the guilty being detected. During OJ Simpson’s trial for the murder of his wife and her friend, for instance, the prosecution showed that Simpson had been physically abusive. The defence, though, argued that “an infinitesimal percentage — certainly fewer than 1 in 2,500 — of men who slap or beat their wives go on to murder them” in a given year, so it wasn’t relevant to the case.

But this is simply the opposite mistake to the prosecutor’s fallacy. The probability that a man who beats his wife will go on to murder her in a given year might “only” be one in 2,500. But that’s not what we’re asking. What we want to know is, if a man beats his wife, and given that the wife is then murdered, what is the chance that he did it?

The scholar of risk Gerd Gigerenzer had a go at answering that. The base rate for murders among American women is about five in 100,000. Assuming the one-in-2,500 probability is correct, then of 100,000 women with abusive husbands, about 99,955 will not be murdered. But of the remaining 45, 40 will be murdered by their husbands. The correct probability that the husband did it should be nearly 90%. Bayesian thinking might have helped convict Simpson.

And although he does not use full Bayesian reasoning but a slimmed-down version, the insight that we should update our existing probability estimates with new information — cumulative monitoring, rather than one-off testing — could, argued the statistician Professor Sir David Spiegelhalter, have spotted both the catastrophe at the Bristol Royal Infirmary and the murders of Britain’s worst serial killer, Harold Shipman, earlier, and saved many lives.

The Lucy Letby case does not turn solely on statistical evidence, and I make no arguments about whether she is guilty or innocent here. But people, including juries, prosecutors and judges, have misunderstood probability in the past; Sally Clark and Lucia de Berk had their lives ruined by that misunderstanding. Thinking like a Bayesian might have helped prevent that.

For studios, the financial appeal is pretty obvious. Each character has its devoted fanbase; if you put two characters in one movie, you can get both of them along to the cinema at once. And because the draw is the characters, not the quality of the movie itself, you can skimp on a decent script or convincing effects.

I was thinking about this as I read The Weight of Nature: How a Changing Climate Changes Our Minds, Brains and Bodies, by Clayton Page Aldern. At the risk of sounding cynical, it struck me as a cinematic crossover event. What are people worried about? Climate change. What else are people worried about? Modern life and mental health! What if we got someone to write a book about… how climate change is damaging our mental health? Then we’d get the climate worriers and the mental healthers buying the same book!

Aldern is not the first person to think of this, and to be fair, climate anxiety is a real problem. Every few weeks a newspaper will run an article by someone saying they won’t have children out of fear that the world won’t be fit to live in, and a global 2021 survey found that more than half of the 10,000 young people surveyed agreed with the phrase “humanity is doomed”. For what it’s worth, I think they’re entirely wrong about that — climate change will have a lot of negative effects on the world, and it’s reasonable to be worried about it, but it’s not going to kill everyone.

On the whole, though, he wants to say something both more subtle and more ambitious than “people are worried about the future”. He wants to say that climate change itself is damaging our brains. He wants to say it’s doing this in obvious ways — we get grumpier and stupider when it’s hot — and in more insidious ones, with the spread of unpleasant diseases and neurotoxins, and, most poignantly, by causing us grief and displacement and loss of identity and all these things which then have various impacts on our brain.

He does this with the weight of academic authority behind him: he is a “neuroscientist turned environmental journalist”, according to the blurb. (Although he does only have a master’s degree in neuroscience. I did a philosophy MA in 2005 but I would feel a bit of a fraud calling myself a “philosopher”.) This makes the book’s flaws all the more baffling, until you remember that it’s a crossover event.

There’s a certain kind of pop-science book that relies on a particular format: story, study, lesson. Christopher Chabris, a psychologist, first noted it in the work of the disgraced science writer Jonah Lehrer. But it’s a staple — you’ll notice it in Malcolm Gladwell books, for instance. You tell some moving, heartwarming or inspiring story; you recount the results of some study which apparently explains what’s going on in people’s brains in those situations; and then you draw them together to give the reader some pat little take-home message.

I rather thought that we had progressed beyond those books, at least in the upper-mid-market pop science titles that might get reviewed in The Times and New York Times, or excerpted in The Guardian. The “this study on 17 undergraduates at Ohio State University shows that actually creativity stems from our right anterior superior temporal gyrus” stuff so rarely withstood the scrutiny of the replication crisis in science that almost all of them turned out to be essentially content-free.

But Aldern follows the formula to the letter. Each chapter starts with a desperate anecdote: a child dying from an amoebic brain disease caught from a swimming pond; a father whose family burned to death in a wildfire. Then there’s a study, usually in zebrafish or fruit flies, showing us how changes to our environment make us forget, or how living near mountaintop-removal mines is associated with higher rates of depression. Then there’s a flowery lesson presented in conclusion: “The weight of nature presses, but we get to press back. A depression is a state, but it is also a refuge, a place from which to come forth.” OK then.

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But what people want from these books isn’t science per se, with its nagging uncertainty and its boring on-the-one-hand-on-the-others. What people want, I think, is vibes: a thrilling sense of fear and sadness, like watching a horror movie or a weepy. (A crossover event between Nightmare on Elm Street and The Bridges of Madison County, perhaps.) That’s my theory, anyway, which would explain why this book can contain claims that are not only under-evidenced but obviously, screamingly false, and still have E. Annie Proulx call it an “important watershed book”.

For instance, Aldern cites one study which looked at the children of mothers who were caught up in Superstorm Sandy in New York and New Jersey in 2012. It found, he said, that among other things, boys who were exposed in utero to the stress of climate change saw a 60-fold increase in ADHD. Think about that for five seconds. What is wrong with that picture? This study found that the prevalence of ADHD in US children and adolescents in 2022 was about 10%. A 60-fold increase in that prevalence would mean that 600% of boys exposed to storms while in utero get ADHD!

In Aldern’s defence, and to my surprise, the study really does claim a 62-fold increased risk specifically for ADHD in boys. But it’s obviously nonsense, a product of small sample sizes made even smaller by chopping them up into bits — boys, girls, boys with ADHD, girls with depression — and picking the most dramatic results. It is the sort of number that should make your eyebrows shoot up immediately. (And in fairness, the study itself only mentions that clearly implausible number down in the results section, and leads on somewhat more credible, though still pretty mad, ones.) Nomura’s study “almost beggars belief”, Aldern says, coming close to, but not quite reaching, an insight.

Aldern also, hilariously, says that a local brewery “saw a gap of 25 degrees Fahrenheit (-3°C)” between indoor and outdoor temperatures at one point. How, you might wonder, can a difference in temperature be positive in Fahrenheit but negative in Celsius? Easy: if you’ve put 25°F into an online temperature calculator and just copied-and-pasted the result. Yes, if the temperature outside is 25°F, that means it’s -3.9°C and pretty nippy. But if the temperature goes up by 25°F, then it can’t go down in Celsius. Again: this stuff should be obvious to anyone who’s paying attention, and that makes me think that no one involved in the book was paying attention.

But this book isn’t a serious effort to find stuff out. Maybe I, as a crashingly literal bore who wants to know how much we know, am the wrong audience. This is a book for fans of the climate crisis and the mental health epidemic, and anything that can bring the two heroes into the picture at the same time is fine.

For instance, Aldern says that “climate change causes amnesia”, because a changing environment triggers forgetting to aid learning. He backs this up with a study in fruit flies and then extrapolates from it to say that people who live near a vanishing glacier in Iceland are therefore… developing amnesia? Except he doesn’t quite say that: he slips into flowery gesturing language. (He often does, usually when it seems like he doesn’t know exactly what point he’s making: “History can be an act of elegy, but perhaps it can be an act of evolution, too.” Righto.) But I’m left baffled. Is the claim that people who live near this glacier will remember things less well, or not?

To be clear, climate change certainly is changing our brains, because our brains change with every new experience we have. That’s what learning and memory is: the subtle rewiring of our brains to store new information. But for most people, climate change — at least so far, in most of the world — is subtle; bad, but subtle. The changes it has wrought in most of our lives so far are minor. The Covid pandemic or the war in Ukraine driving up petrol prices will have changed our environment far more in recent years.

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Or, to be honest, much more quotidian things than that. If you’ve moved house or changed jobs, had a child, started a new relationship or ended an old one, you will have felt the world around you change much more dramatically than anything that climate change has (so far, for most of us) done. And maybe these events do cause us to forget more, or cause our identities to “slip”, as Aldern claims climate change does. But we don’t tend to think of them as fundamentally eating away at who we are. No one’s going to write a book called The Weight of Moving House about the neuroscience of having to remember which box you packed the duvets in.

Climate change is real and will cause real problems. And some of those problems will be psychological: no doubt a lot of people have suffered and will suffer trauma, as changing weather patterns mean their crops die or their houses flood or burn. But this isn’t mystical or subtle, it’s just bog-standard “horrible events are unpleasant to live through”. You don’t need studies about transient forgetting in fruit flies to explain it.

What would be more interesting to examine, perhaps, is the nature of climate anxiety itself. It makes sense to worry about it — as mentioned, it’s big and unsettling, and full of unknowns. But in the West, I think, there’s also a nagging sense of guilt: we drove the cars and built the factories, we polluted the atmosphere, and we got rich doing so. I do wonder if books like this are a manifestation of that guilt: we buy them to show that we’re the good guys, we worry, the climate plagues our thoughts.

The irony is that — precisely because, for most Western people, climate change is still a relatively distant concept — the real impacts of climate change on mental health come via the media, including books such as Aldern’s. But the case that climate change is “changing our minds and bodies” in some subtle, insidious way — any more than all the other changes in our environment — is, to put it mildly, not convincingly made.

But it’s not there to convince us, any more than Godzilla x Kong: The New Empire is meant to convince me that King Kong is really on the loose. It’s just a crossover. Love climate change? Love mental health? Then you’ll love The Weight of Nature!

It’s not true. Oppenheimer never even claimed to have said it. He said that, as the glow faded and the shockwave passed, “a few people laughed, a few people cried, most people were silent”, but that he “remembered the line from the Hindu scripture, the Bhagavad Gita … ‘Now, I am become Death, the destroyer of worlds.’” He did not say it out loud.

What he did say out loud may have been more prosaic. His brother Frank was there, and reported that Oppenheimer simply said: “It worked.”

Christopher Nolan has described Oppenheimer, the subject of his latest film, as “the most important man who ever lived”. The brilliant physicist directed the project that, for the first time, made humanity capable of destroying itself. But it is, frankly, remarkable that he was ever appointed.

The Manhattan Project was perhaps the greatest weapons project in history. At its peak, it employed 125,000 people; half a million people worked on it at one point or another. It spent $2.2 billion, equivalent to somewhere between $30 billion and $50 billion today — six or 10 times the cost of the Large Hadron Collider. It was conducted in utmost secrecy: the German, Japanese and Soviet governments all knew that the US or Britain were themselves conducting nuclear research, as they all were, to a greater or lesser degree — the theoretical possibility had been known for years, and nuclear fission was discovered in Berlin.

It’s surprising, then, that the job of project director was given to Oppenheimer, a psychologically troubled, communist-sympathising attempted murderer with little managerial experience and an abrasive interpersonal style. And yet it was and he was brilliant. As a result of his genius and aptitude, the Manhattan Project was a huge success, not just at its stated goal but in driving fundamental science forward vast leaps in a very short time.

Could that happen today? Would we want it to? What if we were to spend $30 and $50 billion on something similar? The trouble is, we’re sceptical of genius, now. The “great man theory of history” is a phrase used dismissively. Consider the derision when Dominic Cummings called for “super-talented weirdos” to join the government. And even if Oppenheimer was a genius — which he was, and built a team of geniuses around him — someone of a comparable background would surely get nowhere near the top of any major government project.

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Look at the evidence. Oppenheimer was, if not a card-carrying communist, very close to one. He described himself as a “fellow traveller”, and, on the security questionnaire he filled out to join the Manhattan Project, wrote that he had been “a member of just about every Communist Front organisation on the West Coast”.

Later he would say that was a jokey overstatement, but his communist links were deep. His parents were German-Jewish immigrants to New York who had abandoned their faith but kept their cultural identity, joining a secular group called the Ethical Culture Society, whose founder was inspired by Marx. Oppenheimer’s first great relationship was with Jean Tatlock, a Communist Party member; his wife, Kitty Puening, was another communist. The Berkeley Radiation Laboratory where he worked as a young physicist was a hotbed of radical Leftist thought (it was known as the Rad Lab). A friend of his, Haakon Chevalier, had links to Soviet intelligence and may have even attempted to recruit Oppenheimer as a source. Imagine someone now, under investigation by the FBI for links to Chinese intelligence or some extremist political movement like the Proud Boys or radical Islamism, being put in charge of the development of a new stealth fighter jet — or, perhaps more analogously, an AI-based weapon system.

There were other reasons to think Oppenheimer was a strange choice for director. For one thing, he had no significant managerial experience, certainly not of big projects. For another, he was difficult, abrasive, socially awkward and — in modern terms, at least — deeply mentally unwell. He was sexually frustrated as a youth and angry at others’ happiness. One time, he saw an amorous couple on a train and, according to his school friend Francis Fergusson, when the man left the carriage, Oppenheimer moved to kiss the woman himself. Then he “was at once overcome with remorse, fell on his knees, his feet sprawling, and with many tears, begged her pardon”. But the remorse did not last long: He saw the woman beneath him on the stairs and, bitterly, “was inspired to drop his suitcase on her head. Fortunately, he missed.”

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That violent urge was not a one-off. Oppenheimer, as a youth, lacked control of his emotions, and seems to have twice attempted to kill people. That same friend, Fergusson, told Oppenheimer that he had got engaged to his girlfriend: “I leaned over to pick up a book, and he jumped on me from behind with a trunk strap and wound it around my neck.” Fergusson fought him off, but “was quite scared for a little while”. Oppenheimer then “fell on the ground weeping”, again.

And at Cambridge, where Oppenheimer studied as a postgrad, he seems to have left a poisoned apple on the desk of his supervisor, who had attempted to get him to do more experimental work. (Oppenheimer, though a brilliant theorist, had an amazing ability to ruin every experiment he came near.) He was very nearly expelled, and only avoided criminal charges after his father lobbied the university and Robert agreed to psychiatric counselling.

Oppenheimer himself would say much later that “I was on the point of bumping myself off”. He suffered from depression all his life; he was diagnosed after the poisoned-apple incident as a “hopeless case” of “dementia praecox”, a now defunct diagnosis which is usually now associated with schizophrenia. He probably didn’t have schizophrenia — depression can cause delusions — but he was certainly unstable. And it clearly started to grate: in Göttingen, his fellow students signed a petition saying they’d boycott the class if he didn’t shut up in seminars. At the Rad Lab he would interrupt guest speakers, saying impatiently: “Oh come on now! We all know that. Let’s get on with it.” He sounds, in short, like a bit of a dickhead: rude, self-centred, pushy.

All in all, as discussed, he was not an obvious choice to run a multi-billion-dollar top-secret superweapon project involving hundreds of thousands of people. But General Leslie Groves saw something in Oppenheimer which not everyone else could. Groves would say of him in his memoir that he had “two major disadvantages — he had had almost no administrative experience of any kind, and he was not a Nobel Prize winner”. He noted an “overweening ambition”, a breadth of knowledge, and a considerable genius.

But Oppenheimer wanted something more than scientific discoveries — he wanted glory, fame. His friend Freeman Dyson, another great physicist, would say later: “He always wanted to be at the centre. This quality is good for soldiers and politicians but bad for original thinkers.” He lacked, in Dyson’s phrase, Sitzfleisch. It means “sitting flesh”, backside, but metaphorically the ability to sit still and finish something. Instead, he was “driven by an irresistible ambition to play a leading part in historic events”.

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This made him a flawed scientist. Dyson tells how Oppenheimer invented the concept of black holes in 1939, decades before Stephen Hawking would make them famous. But having made the insight, he never returned to it. He got distracted by other, more fashionable things.

But it made him a peerless leader. “When he was present at the centre of action,” said Dyson, “he rose to the occasion and took charge of the situation with unexpected competence… He was astonishingly effective as leader of the [Manhattan] project.” His dilettante interests meant he could understand the engineers, the physicists, the chemists, the metallurgists. His inability to focus on a topic was less of a problem if he could order an underling to focus on it for him.

Groves saw this. Under Oppenheimer’s guidance, the project took the American nuclear programme from essentially nowhere to completion within three years. When he joined, there was no nuclear programme to speak of. Scientists were imported from Europe, especially Britain and Hungary. Everything had to be developed from scratch, and it was: the Manhattan Project created thousands of inventions. The patents it processed were secret, of course, but if they had been filed, then by the end of the war they would have represented 1% of all existing patents. Nuclear science progressed leaps and bounds in the project’s laboratories, as did digital technology, materials science, and a hundred other disciplines.

Why did it work? Oppenheimer can surely take much of the credit. It was Oppenheimer who recognised that the firing mechanism used for a uranium bomb, one of the two models used, would not be as effective in a plutonium bomb, and called for another system, an “implosion” system which blasted two hemispheres of plutonium together with high explosive. It was also he who recruited John von Neumann, one of the greatest geniuses of the 20th century, who in turn developed the perfectly shaped lenses of high explosives required to slam the plutonium together with perfectly even pressure all around the sphere, a procedure one scientist compared to trying to crush a beer can without spilling any of the beer.

We can’t know the counterfactual. Perhaps other, less unstable, less communist-sympathising project leaders were available. Perhaps they would have done as good a job: after all, the project “brought together the greatest concentration of scientific luminaries working on a single project that the world had ever seen,” as Alex Wellerstein wrote. As well as Oppenheimer and von Neumann, the project had at its disposal Edward Teller, Leo Szilard, Enrico Fermi, Vannevar Bush, Richard Feynman, and many more.

It also had something like 1% of the entire US civilian labour force and 1% of the country’s electrical power at its disposal. Maybe you could have put anyone in charge of that and it would have done well, much as any idiot can probably manage Real Madrid sufficiently well to win most football matches: just tell all your stars to go out and play football. But Groves took a chance that perhaps not many others would, one that would leave him hanging in the wind if it went wrong.

Again, imagine it now: the government appointing someone with a known track record of violence and delusional mental illness, placed in charge of a vital defence project. It would be near-impossible: none of the “weirdos” Dominic Cummings wanted to appoint to government (and I’ve met a few of them) was remotely as weird as Robert Oppenheimer. No Communist Party-adjacent attempted murderers there. Of course, Groves had the dictatorial authority granted by war — the US was committed to the defeat of Germany and Japan and committing its entire industrial base to the task; the perfect situation for big, dramatic bets like Manhattan — but still it was bold and dangerous.

In a weird way, I’m reminded of the appointment of Kate Bingham to the UK’s Vaccine Task Force: She was well-qualified, and again the emergency situation gave the government leeway to make snap decisions that they wouldn’t otherwise have had. But her political connections — she is married to a then-minister, Jesse Norman — made her a target: the Good Law Project sued, calling her appointment “jobs for their mates”. The British vaccine procurement was a roaring success and Bingham’s appointment was vindicated, but if it had not been, then the decision would have looked dreadful. (Please do not take this to mean that I think Kate Bingham is the same as Robert Oppenheimer.)

There are plenty of big scientific projects around today. But nothing on the scale of Los Alamos: Artemis, the successor to the Apollo moon missions, has perhaps 30,000 employees. (Apollo itself, driven by Cold War anxiety and national willy-waving, was bigger even than Manhattan, with nearly half a million staff at its peak.) Perhaps the exciting work has moved into the private sector, but even the cutting-edge AI firms employ a few hundred or a few thousand people, some far fewer. Midjourney, the best of the art AIs, has a staff of a dozen or so. There are plenty of strange geniuses around — Demis Hassabis, the video-game-designing chess savant behind DeepMind, is my favourite — but nowhere has the kind of world monopoly on them that Oppenheimer established.

Nonetheless, there’s a parallel with modern AI. Oppenheimer seemed to want the responsibility of having created the bomb: he said that the physicists involved in the project had “known sin”. Von Neumann responded, pithily, that “Sometimes someone confesses a sin in order to take credit for it.” When AI researchers now talk of the threat of AI, how it could destroy humanity, there is a hint of that – what we are doing is so amazing and powerful that it could destroy the world, aren’t you impressed? Of course, that doesn’t mean that it’s not true – I think there’s a decent chance AI could destroy the world, and developing nuclear weapons really was a morally ambiguous thing to do. But people who want to be great can scratch that itch by being great and terrible, as well as great and good.

Oppenheimer was not ashamed of his work, even if, for PR reasons,  he sometimes wanted to appear so. He “continued for the rest of his life to be proud of his achievement,” Dyson wrote. And when a German playwright depicted him as a tragic hero who regretted his actions, he protested bitterly. The line from the Bhagavad Gita about death was a later amendment. Years earlier, he had quoted a different one: “If the radiance of a thousand suns were to burst at once into the sky,” he said, “that would be like the splendour of the Mighty One.” That does not sound like a man who feels remorse.

Its latest such gambit is a claim that the Office for National Statistics’ data shows “a significant rise in the death rate for adolescent males over the last eight months”. HART have put together an “open letter to Sajid Javid, Chris Whitty, Patrick Vallance and the relevant public health bodies”, saying that “the concern is that this time period coincides with the rollout of vaccinations to this age group”.

There’s a saying in journalism that you should play the ball, not the man. And I will play the ball, but I want to play the man a bit first. HART has form for suggesting that vaccines kill people: on p35 of its ‘Covid-19 Response’, still prominently linked on their website, they suggest that the second peak in deaths “coincided with the mass roll-out of novel mRNA/DNA vaccines” and that “whilst we cannot infer causation from correlation, the mRNA/DNA vaccines had not been tested on this cohort” and it would be “negligent” not to ask whether “the rise in deaths during this period is linked in some way to the vaccine roll-out”. (The second peak in deaths was, in fact, caused by the second peak in Covid cases which preceded it by a fortnight or so.)

HART is populated by odd people who believe strange things: one of its publicly listed members worried, in a since-leaked private chat, that the vaccines were literally making people magnetic. Other leaked messages show a member suggesting that the group “seed the thought that vaccines cause Covid”, and that the vaccine rollout is “a crime against humanity” and part of a plan for “massive scale depopulation”. It’s worth noting that while these people may seem to be cranks, they also seem to have some influence with senior Tory backbenchers.

But let’s look at the ideas, as well as the people. After all, stopped clocks are right twice a day, and there have been plenty of stopped clocks in the pandemic. So: what’s the evidence that vaccines have been killing young people?

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Here’s HART’s case. They say that according to ONS data, there were 402 deaths among 15 to 19-year-old boys between May and December last year, and that that’s about 65 more than the average for that period over the years 2015 to 2019. I’ve looked into it, and these numbers are about right. In fact, they are a slight understatement of reality: you can see the figures for 2021 here, and the five-year average here. This is, the ONS acknowledges, a “statistically significant” increase.

HART thinks, or at least heavily hints, that the extra deaths are caused by myocarditis, an inflammation of the heart muscle which has been seen in rare cases following vaccination with the Pfizer jab: “​​a connection cannot be excluded,” they say, and it would be “a grave dereliction of duty” not to investigate. They took the ONS to the High Court to get them to release more data, including the age, gender, and vaccination information of everyone who died between the age of 12 and 19 since 1 May last year[1. The ONS is looking into it, but they can’t release the data easily: so few people die at that age that if they gave much more information about them then individuals would be identifiable. The High Court agreed that they shouldn’t release it.].

HART are not the only people to worry about this. In the US, there are regular outbreaks of concern around the mRNA vaccines and myocarditis. But is there anything in it?

For the record, HART is right that the number of recorded deaths in that age category is unusually high. I spoke to the ONS, and they agree — a spokesperson told me that there has been an increase of recorded deaths, compared to the five-year average.

That said, it’s not clear that the rise is real. The ONS only becomes aware of deaths when they are officially registered, which — if the death is referred to a coroner — could be weeks or months after the death itself. Young people’s deaths are much more likely to be referred to the coroner, and in 2020, coroners’ offices were under enormous strain from the pandemic. The spokesman also speculated that there might be a simple backlog from the previous year. There may be all sorts of other reasons, and until the coroners’ reports are in we won’t know.

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Another problem for the myocarditis hypothesis is that, although HART says that the “time period coincides with the rollout of vaccinations to this age group”, it doesn’t really. The first mass vaccinations for under-18s didn’t start until August, and even then only began relatively slowly: only about 40,000 people a day were being given first doses in that period, down from a high of 600,000 in March. HART also says that the real risk is second doses, and under-18s wouldn’t have received those until September. Admittedly, 18 and 19-year-olds would have had their jabs somewhat earlier, but not that much earlier: even 30-year-olds were unable to have theirs until the end of May.

And looking at the data that HART themselves cite, there doesn’t seem to be an obvious spike after younger people get the vaccine: while the number of deaths does seem to go up after about May, there’s no spike in August when large numbers of young people started getting vaccinated.

The ONS spokesperson said that there have been adverse vaccine reactions mentioned on death certificates — but only 13 of them, and the ages ranged from 35 to 94. Perhaps there’s been a rash of unreported myocarditis cases, or other vaccine reactions, among young men, but it would be a bit surprising given the attention on the issue.

But let’s grant HART’s premise. Let’s imagine that some percentage of these excess deaths are not statistical artefacts, and are caused by myocarditis. What would that mean?

The main conclusion wouldn’t change: vaccines save lives. Look at this Our World in Data page on the risk of death for vaccinated vs unvaccinated people, in various countries. An unvaccinated American is around 100 times as likely to die as a triple-vaccinated one; an unvaccinated Swiss is about 165 times as likely to die. Similar if less extreme numbers can be seen in Chile and England, although OWID doesn’t yet have data on the effects of boosters in England.

Of course, that’s across all ages. But when you look at 12 to 17-year-olds in the US (the only country where there’s a reasonable amount of data), about one in every 100,000 unvaccinated died each week. That would be about 250 a week, if everyone were unvaccinated. It’s well over ten times the risk to vaccinated children of that age.

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The US CDC assesses the risk like this: if 100,000 children are vaccinated, they’d expect around five to seven cases of myocarditis, likely mild. But if they weren’t vaccinated, they’d expect about 5,500 of them to be infected with Covid in the following three months, and about 50 of them to be hospitalised. Out of the 25 million or so 12 to 17-year-olds in the US, that would be around 12,000 hospitalisations in three months. Perhaps ironically, a fair number of them would develop myocarditis: the UK government says about 18% of hospitalised Covid patients suffer myocardial injury.

The point is: even if some non-trivial number of young people have died of myocarditis because of the vaccine, the odds are that more would have died without it — and likely very many more would have suffered a really nasty illness and ended up with some quite long-term effects from it. And, of course, they’d have been more likely to pass it on to other, more vulnerable, people, including their own families.

It is easy to say “even one death is too many”. Of course, each death is a tragedy; but two deaths is twice as big a tragedy, all else being equal. I hope that the ONS is able to find some hint of what caused the apparent increase in deaths among adolescent males; I sincerely doubt that it’s the Pfizer vaccine. But even if it is, the likelihood is that it saved many more lives than it cost. 

Luckily, I think the battle over vaccines has largely been won — more than 90% of UK adults are now vaccinated. But groups like HART have influence over serious people on the fringes of real power, despite being deeply strange people who think those who advocate vaccines for children “need shooting”.

The trouble is, HART’s data is superficially convincing, and takes work to rebut. There are still many thousands of children, and others, who have yet to have their vaccine: if some of them die unnecessarily because of this nonsense, that really would be a tragedy.

And then the whale is gone, and the creatures go back to their lives of crushing pressure, constant darkness, and an eternal knife-edge struggle for survival. As Thomas Malthus had it in his bleak vision: organisms, which increase exponentially in number, will rapidly outgrow their resources, which can only grow arithmetically. So of course, the excess population which has grown up on this brief glut must die off

We are currently living in a time of whalefall, suggests the scientist Vaclav Smil in his new book, How the World Really Works. He doesn’t use the word, of course: credit for the macabre whale metaphor must go to Scott Alexander. But modern humans are animals, products of evolution like any other, and yet we noticeably do not spend every minute of every day struggling to get the material required to survive. Instead, we build cathedrals and watch football, we make art, we waste time on Twitter. And that is because we live on the gigantic, blessed whale carcass that is our fossil fuel inheritance.

For Smil, our discussions about climate and energy are hamstrung, because so few people actually understand how the world really works. Material lands in front of us in pre-packaged, convenient forms — shrink-wrapped pork chops, winter strawberries, lights that turn on when you flick a switch, phones made of plastic and metal. The world is a set of black boxes that we use but, in most cases, do not understand. So when we say “we need to cut back our carbon emissions”, most of us don’t really grasp the implications of doing so.

But somehow, all these incomprehensible processes are keeping us alive, and we should find it astonishing that they are able to do so. The demand for material – for energy and nutrients – is greater than it has ever been. The world’s population has exploded: in 1800, there were about 1 billion humans. In 1950, there were 2.5 billion. Now there are 7.7 billion. In my parents’ lifetime, the number of humans alive has trebled. But amazingly, the amount of material available to each of them has increased even more, and that is in large part because of our use of fossil fuels.

In 1800, almost all the energy used globally was in the form of human and animal muscles, for mechanical work, or plant matter, burned for heat and light. Coal, the first widely used fossil fuel, was just starting to be used in steam engines in the UK, but it was negligible overall. By 1900, fossil fuels were the source for half our energy. By 2000, they were the source of 87%.

And as a result, our lives have been transformed. The amount of energy available to the world has increased 1,500-fold. That is only part of the story, though: increased energy efficiency means that the gain in useful energy is more like 3,500 times. And even though the world’s population has gone up many times, “an average inhabitant of the Earth nowadays has at their disposal nearly 700 times more useful energy than their ancestors had at the beginning of the 19th century”.

But most of us don’t realise how that energy is actually used. A large percentage, for instance, is used to create four materials which are the building blocks of modern society – materials which are so ubiquitous that we barely notice them, even as we depend on them.

Smil identifies these four basic pillars of human civilisation as steel, cement, plastic and ammonia. Producing them takes enormous amounts of fossil fuels. It takes, for instance, 25 gigajoules of energy to produce one ton of steel, roughly twice the amount of energy used by the average UK household per year. In 2019, the world used 1.8 billion tons of steel; its production is responsible for about 8% of the world’s total carbon emissions. But we can’t do without it: the frameworks of our cities are built of it; the pipes we send our water and gas through, too. Our cars, our transporter ships, our knives and cooking pots. Our machines for making all these things. Cement and plastic are similarly vital, and are responsible for comparable amounts of our total carbon output. We can’t do without them, and there’s no easy carbon-free alternative way of making them.

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And then there’s ammonia, which rarely features in any conversation about cutting carbon emissions. Ammonia is a nitrogen atom ringed by four hydrogen atoms. Our atmosphere is 80% nitrogen by mass, but plants – which need it for growth – can’t easily take it out of the air. Instead they need to gather it from the soil. Bacteria that live in the roots of some plants can “fix” it into the soil; animal wastes like manure have relatively high nitrogen content. But those methods can only support a certain amount of growth.

In the beginning of the 20th century, a German chemist called Fritz Haber invented a process for getting nitrogen out of the air by making ammonia. It requires huge amounts of energy, and hydrogen, usually taken from natural gas. We now spread hundreds of millions of tons of ammonia on our fields — about 50% of the total nitrogen going into food production comes from it. Smil quotes an author, writing in 1971: “industrial man no longer eats potatoes made from solar energy; now he eats potatoes partly made of oil.”

This means the world is able to eat. The share of the global population that is underfed has plummeted, even as the actual population has ballooned – about 65% of people could not get enough to eat in 1950, compared to about 9% in 2019. So, “in 1950 the world was able to supply adequate food to about 890 million people,” as Smil puts it: “but by 2019 that had risen to just over 7 billion”. That is not entirely down to ammonia, but ammonia is a large part of the story. If fertiliser were removed, perhaps half the world’s population would starve.

Agriculture, then, depends on the whalefall: the glut of energy provided by fossil fuels. Our deep reliance on fossil fuels, to create materials most of us don’t appreciate we need, is unnerving. Especially when Smil points out that much of the world — notably, sub-Saharan Africa — lives on well below average levels of energy use. Africa uses just 5% of the world’s total ammonia supplies, despite having almost 25% of the population. About 40% of the world — 3.1 billion people — has a per capita energy supply “no higher than the rate achieved in both Germany and France in 1860”. “In order to approach the threshold of a dignified standard of living,” writes Smil, “those 3.1 billion people will need at least to double — but preferably triple — their per capita energy use.”

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Can we do that while also reducing our carbon emissions? Not fast, says Smil. For all the boasts and pledges — all the “government targets for years ending in zero or five”, about which Smil is very sniffy — the world relies too heavily on fossil fuels, for too many things, to rapidly stop using them. Even the International Energy Association’s optimistic “Sustainable Development Scenario” projects that the share of fossil fuels in the world’s energy mix will only drop to 56% in 2040

We can, and need to, replace fossil-fuel energy sources with renewable ones. But there are obstacles, beyond simply the political will. Renewable energy is very good at making electricity. But electrical energy isn’t ideal for making the incredible heat needed for iron and steel production, or cement. The Haber process for making ammonia works much more efficiently with natural gas as the source of hydrogen and energy than it does with water and electricity.

And away from the four pillars, fossil fuels have other huge advantages. It’s very energy-dense: you can store much more energy in a kilogram of kerosene than you can in a kilogram of battery, meaning that transatlantic flights are possible. And it keeps — currently, there is no suitable way of storing electrical energy for more than a few hours or days, so solar energy stored up in summer is no use in winter. A barrel of oil will last indefinitely. Those facts will change, and Smil is more downbeat than I am about how quickly that will happen, but we are definitely going to be relying on fossil fuels for some decades yet.

But unlike the crabs and hagfish that eat the fallen whale, we are clever, and we needn’t simply slink back into the darkness and starve. Smil thinks there are major gains in efficiency which can be had, over and above the enormous gains so far. He points to water use as an example: in 2015, the US only used about 4% more water than it did in 1965, but in the meantime, its population had gone up by two thirds actual per capita water use has dropped by 40%, even while the country has got richer and better fed. Perhaps similar efficiencies can be found with energy and carbon.

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Besides, we are not about to run out of whale, at least not imminently: the raw materials — metals, fuels — that our lifestyle needs are still around in large amounts. But we have grown in numbers and lifestyle well beyond the capacity of the pre-whalefall world. And we don’t want to go back to the lifestyle we had before, even if some romantics and millenarians might disagree. In fact, we want many more people to enjoy the spoils of whalefall. We have used fossil fuels to construct an astonishing world, one that feeds and houses an incredible number of people.

We need to wean ourselves off fossil fuels, and the sooner the better. But it will be a long and difficult job — as Smil demonstrates, they are threaded through our society at every level, entwined like knotweed in the systems that provide our food, our housing, our machinery, our transport. We forget how complex our society is until it stops working in some way — as when supply chains broke down in the pandemic and our hospitals ran out of rubber gloves (an issue Smil talks about in a section on globalisation). As it stands, if we were to reduce fossil fuel consumption by the sort of degrees that some demand, it would lead to disaster, because we haven’t unpicked the threads yet.

At the moment, they’re free, at least for users. The Government will provide them, in packs of seven, to anyone who asks. But is that going to stop? There was confusion over the weekend after one “senior Whitehall source” told the Sunday Times that it would — before the education minister, Nadhim Zahawi, informed Sky that there were no such plans.

Maybe it’ll happen soon, maybe it won’t. But we are moving towards a world in which we need to think differently about testing for Covid; the current scenario — free tests for anyone who wants them, followed by automatic isolation if you have a positive result — cannot continue forever.

The question now is: what should replace it? Will it soon be time to start using tests in new ways – to let people decide whether they ought to stay home, rather than ordering them to via restrictions? And will we start doing the same for diseases other than Covid?

First, let’s be clear about something: the cost of LFTs has been low. The figure usually quoted is £6 billion — which seems tiny when compared to the £315 billion spent on Covid measures (as of December 2021), not to mention the vast economic damage inflicted since March 2020. Economists often talk about “Treasury brain”, the desire to cut any and all spending without asking whether that spending is actually saving money elsewhere. I suspect the call to stop giving away LFTs is an extreme case of Treasury brain: lateral flows don’t have to stop all that many infections to pay for themselves many times over.

And they probably do stop a lot of infections. LFTs are less sensitive than the standard PCR tests, but they are much quicker — minutes rather than hours — and, unlike PCRs, will not detect dead virus in the system of someone who has been recovered for days or weeks. “I’ve always been a big advocate,” says Prof Alan McNally, a microbiologist at the University of Birmingham. In particular, they are more likely to return a positive result when you have a high viral load and, therefore, are more likely to be infectious. He points to various studies, including this modelling paper in the Lancet Public Health journal, which seem to find that LFTs followed by self-isolation lead to significant drops in transmission.

That said, we should still be wary. LFTs definitely miss some non-trivial fraction of cases, including some which are infectious. Alexander Edwards, a professor of biomedical technology at the University of Reading, notes that one recent preprint followed 30 people who had tested positive for Covid on PCR while doing LFTs at the same time. It found, as expected, that PCRs tended to pick up the infection earlier and more reliably. But it also found, through contact tracing, that four people passed on the virus in between negative LFTs. It’s a tiny study, and we have no way of knowing how common events like that are. But they do happen: you can’t 100% rely on LFTs telling you whether you’re infectious or not.

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But LFTs have always been in a bit of a strange situation. On the one hand, there is a tendency to assume that they are outranked by PCRs. I know at least one person who had a positive LFT followed by a negative PCR, and assumed that meant the LFT was wrong and they could go back to work. But that’s wrong. “I’m always astonished by this,” says McNally. “If you have symptoms, and you take an LFT and it’s positive, then you have Covid. The idea that PCRs trump LFTs is baffling.”

There have been calls recently to drop PCRs altogether — for Test & Trace to rely entirely on LFTs — and that makes sense to me: even if PCRs are somewhat more sensitive, they’re much more expensive and they take two days to give you a result. An imperfect result now is better than a somewhat more accurate one after you’ve been spreading the virus (or needlessly locked at home) for two days.

On the other hand, there’s a contradictory tendency: to think of LFTs as definitive. I notice this in myself: I’ll say things like “Ah, good thing I don’t have Covid!” when I test negative, rather than virtuously thinking “Well, this new evidence supports my hypothesis that I do not have Covid.” No medical tests are 100% accurate — some number of negatives (and positives, although that’s rarer with Covid) will be false. “I’ve always been nervous that people will think that if they have a negative PCR it’s fine to go and see their family,” says Edwards. “And that’s not true.” He worries that overenthusiastic supporters of LFTs “overemphasise how good they are”, and potentially undermine trust in them when false negatives happen.

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But we are moving into a new phase of the pandemic, and trying to return to as normal a life as possible. At the moment, Covid is a “notifiable disease”: that is, if you are diagnosed with it, the Government must be informed. But if we are to “live with the virus”, then at some point that will change; we don’t inform the government when we have a cold, or the flu. Likewise, we’ll need to stop informing the Government of every asymptomatic infection or mild case of Covid-caused sniffles.

It’s unclear when that will be, although my own feeling is that it should be relatively soon: a few months’ time, at most, assuming future variants aren’t much more dangerous. Certainly, we should be thinking about reducing the isolation period for positive tests, and letting people make their own judgments about isolating. (We implicitly do this already, since the whole thing runs on an honour system: if you want to fake a negative LFT test, it is trivially easy to do. We’re relying on people’s honesty as it stands.)

But once we do move away from notifying the Government about Covid cases, LFTs are still going to be a vital part of the world. We will need to think of them neither as PCRs’ inadequate cousins nor as golden tickets allowing us to enter our elderly relatives’ nursing homes free of danger, but as useful tools in our risk assessment.

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If you’re going to visit your elderly parents, and you’re all triple-vaccinated, and you have no reason to think you have Covid, then you might feel pretty safe already and use an LFT to add an extra layer of reassurance. Likewise, shops, pubs and theatres might also want to call for customers to show a negative LFT result in order to reduce the number of Covid infections among their patrons and staff — it won’t remove every one, but it will reduce the risk.

What’s going to be really interesting, though, is how Covid changes our attitudes to diagnostic testing in general. “It has democratised it,” says McNally. Before now, infectious-disease testing was something done in labs, by doctors and scientists. But now we’re all extremely capable of sticking a simple cotton swab up our nose and using that to test for Covid, at home.

But we don’t have to do that just for Covid. I recently spoke to Pantelis Georgiou, a professor of electrical engineering at Imperial College London. He’s developing what seems to be the natural successor to LFTs: “lab on a chip” technology that can test for a variety of pathogens, whether viral, bacterial or other, as quickly as LFTs. It has been used to detect malaria in Ghana, and, he told me, has been shown to work for TB, aspergillus and dengue, among other other things.

The most interesting thing to me, though, is that it doesn’t need to be specific to a single disease. “We are working to roll out a version that looks for the top five respiratory or fever-causing pathogens,” he told me. So you could do a simple swab test, and it will tell you in minutes whether the cold symptoms you have are Covid, or the flu, or a rhinovirus.

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“I think it could really change how people regard having symptoms,” says McNally. For instance, imagine you often get snotty, and you’re not sure whether it’s just allergies or a virus. In future, you might be able to test, and use the result of that test to help you decide what to do — you might feel comfortable going into the office with a confirmed cold or a negative result, but not with an influenza virus or Covid, for instance.

That’s for the future. For now, LFTs do a good job, as long as we see them as risk-reduction tools rather than infallible oracles. “The actual cost of producing one is pennies,” says McNally — most of the £5-or-so price tag at the moment represents the cost of building factories, and logistical issues like boxing and transport, costs which will fall. “You could see a situation where you can buy a pack of seven for £10, and that would really change things.” In future, maybe they’ll be like paracetamol, just something that everyone has in their cupboards.

For now, I suspect that keeping them free is the best option, contrary to the Treasury-brain instinct — although perhaps some nominal cost would stop people stockpiling them when demand is high. Rather, the next change should not be to how we provide them — it should be to how we react to them. And that is to rely on the results, rather than double-checking them with PCR. Meanwhile, with a 90% vaccinated population, we can surely put an end mandatory isolation, and start trusting people to make their own decisions about risk.

That’s not necessarily because they’re wrong. It’s because they are imprecise: the pundits won’t fully commit to anything. “The year ahead will be difficult for Boris Johnson,” that sort of thing. Whatever happens, it’ll be hard to say whether that prediction came true or not.

What’s much more worthwhile is making clear, well-defined, falsifiable forecasts, with fixed timetables: forecasts that commit you to say “OK, I got that wrong.”

I had a go here, here and here during the pandemic (an embarrassing number of which have been wrong). But since it’s the new year, I thought I might stake my own reputation on a few forecasts for 2022.

To do so, I’m going to use the Phil Tetlock “superforecasting” model, of giving lots of forecasts with levels of confidence. So I might say it’s 60% likely that Boris Johnson will have another child in 2022, or that there’s only a 10% chance that Keir Starmer will be prime minister by the end of the year. At the end of the year, I’ll look at all my forecasts. Ideally, about 60% of my 60%-confident forecasts should have come true, and about 10% of my 10%-confident forecasts, and so on[1.  Ideally ideally, I’d have made hundreds of forecasts and you could get a really good sample to see how well-calibrated I am. But unfortunately that’s not going to happen, because calculating these takes ages.].

I’ve used the prediction site Metaculus for my questions. When the site had a good question on it already, I’ve used that; when I needed to make up my own, I’ve done that. I’ve linked to the questions so you can make your own predictions and see what the wider Metaculus community thinks.

Coronavirus

I think that this year will see things return pretty much to normal in Britain. Every so often there’ll be a bit of a flare-up, and some people will call for an immediate return to mandatory masks and closed pubs and all that. But 90% of the population aged over 12 has now had at least one jab and 60% have had three, and most people have probably had the actual disease as well (13 million confirmed cases, which is likely an undercount by at least 60%). So the actual risk to British people is low.

That said, about 100 people are still dying every day with Covid. That sounds bad, and it is, but it’s only about one in every 16 deaths in the UK. Still, if it carries on at that rate for the whole year, we’d expect another 35,000 to 40,000 deaths this year. It may not carry on at that rate all year, but I can’t imagine it’ll drop so much that we see fewer than 20,000. So I’d say it’s 75% likely that we see 20,000 or more deaths within 28 days of a positive Covid test in Britain in 2022.

Also, I think we’ll get a few scares from new variants. The WHO has announced five “variants of concern” since the pandemic began, the most recent being Omicron. Given a base rate of about 2.5 a year so far, it’s pretty likely that we’ll see more this year. Whether any of them will be as devastating as Delta or Alpha is a bigger question, but I’d say it’s 90% likely that the WHO will announce at least one new variant of concern in 2022.

Relatedly, if new VoCs do arise, will one of them overtake Omicron as the most commonly detected strain of Covid in the world? Again, I think this is pretty likely – I’d say there’s only a 30% chance that Omicron will be dominant by next January. Six weeks ago we’d never heard of Omicron and Delta was dominant; the original Covid was overtaken by Alpha only eight months or so after it was declared a pandemic. So far no variant has remained dominant for more than a few months and Omicron appears to be petering out in South Africa.

Predictions:

More than 20,000 people will die within 28 days of a positive Covid test in the UK in 2022 (75%)

The WHO will add another variant to their Variants of Concern in 2022 (90%)

Omicron will be the globally dominant strain of Covid at the end of 2022 (30%)

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Vaccinations

The UK’s booster campaign has rolled out with impressive speed since it was announced in September. But there have been suggestions that we will need to do annual Covid jabs, as we do for flu. So I predict it is likely (65%) that we will offer fourth jabs to healthy British adults before the end of 2022.

Not that I think we should do this. I think the vaccines we’ve already had will probably protect people very effectively against severe disease and death even once the measurable antibodies have declined, and I think each new jab will do lots more good in the arms of unvaccinated people in lower-income countries than they will in the arm of a triple-jabbed pensioner in Reading. But given that governments have a duty to look after their own citizens, and, more pressingly, a need to curry favour with those citizens to avoid being voted out, I think it’s pretty likely that we’ll do it. Metaculus forecasters think it’s likely in the US, as well.

The important thing will be whether we vaccinate the world, and especially the poorest countries. It should be said that despite a widespread and understandable feeling that the West has hoarded all the vaccines, almost 60% of the world’s population has received at least one jab, and even lower-middle income countries (which include places like Bangladesh and Egypt) have vaccinated more than 50%.

But the truly poor countries – places like Afghanistan, Sudan, North Korea and many sub-Saharan nations – are lagging badly behind, at only about 8.5% coverage. It’s been speeding up, and in richer countries vaccinations have followed an S-shaped curve – slowly, then quicker and quicker, then slowing down again. But looking at the curve on the Our World in Data page, it looks like the low-income countries are taking off at about a quarter the speed that the rich ones did.

It took rich nations about six months to get to 50% coverage: the world makes vaccines faster now, but also there are still huge logistical and incentive problems for getting those vaccines to poorer countries. I’d guess that between 30% and 60% of inhabitants of the world’s poorest countries will have had at least one jab by 2022. I think it’s also pretty likely (75%) that at least one country will still have fewer than 10% vaccine coverage, just because there are a lot of poor countries, some of which have basically no working health infrastructure.

Predictions:

The UK will authorise fourth doses of Covid vaccines for healthy adults in 2022 (65%)

What percentage of people in low-income countries will have had at least one dose of a vaccine by 2023? – between 30% and 60%

At least one nation will have less than 10% of its population vaccinated with at least one dose by 2023 (75%)

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Can we trust the climate scientists?

By Tom Chivers

Climate

The world will continue to warm, on average, over the next few years. There’s too much carbon in the atmosphere already for that to change.

But there’s a lot of variability from year to year. Most years are somewhat hotter than the one before, but not always: of the 20 years leading up to 2020, 13 have been hotter than the previous year, and seven have been cooler, according to the Met Office. So the base rate is that it’s about 65% likely that 2022 will be warmer than 2021.

That said, 2021 has been a relatively cool year, due to La Niña, an atmospheric and oceanic phenomenon that brings cold water to the surface of the oceans. La Niña finished some months ago, so it shouldn’t affect 2022. I think it’s therefore very likely (85%) that 2022 will be hotter than 2021, when the data eventually comes in.

Prediction:

The global average surface temperature in 2022 will be higher than it was in 2021 (85%)

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The men who failed Britain

By Tom Chivers

Energy

There’s a cruel joke that goes to the effect that “fusion energy is 30 years away and always will be”. That said, in the last few years, there’s been a lot more excitement about it, and several new companies have started up, with nearly two billion dollars of funding backing them.

It’s very unlikely that fusion energy will reach any of its milestones – “ignition” or “breakeven” or profitability or anything – this year, and pretty unlikely that they’ll do it this decade. But I do think that the excitement will carry on building. According to a survey by the Fusion Industry Association, there are “at least 35” fusion companies around the world, most of which have been founded in the last 10 years. I think it’s likely (65%) that number will go up in 2022, as the technical questions continue to be answered, and as investors and entrepreneurs get more excited about the possibility of a viable, profitable industry.

Also, the cost of solar energy has plummeted over the past decades. It will almost certainly continue to do so, but since it is already cheaper than any form of fossil fuel, cost is no longer the sticking point. Now, the bottleneck for renewable energy is making it available when the sun isn’t shining or the wind isn’t blowing – having loads of sunshine in August is no good for heating your house in December.

There are various ways of doing that, such as installing cables between countries, so the UK’s offshore wind can supply Spain when it’s windy, and Spain’s solar can supply the UK when it’s sunny. But the most obvious is storage, and the most obvious form of electricity storage is batteries.

The cost of electricity storage has also dropped enormously – according to Bloomberg, it’s gone from a global average of about $300 per megawatt-hour in 2018 to $138 in 2021, mainly driven by the electric car industry. Despite supply chain issues and increased cost of various raw materials, mainly metals, I think that’s probably going to carry on – I think it’s 80% likely that the next Bloomberg report will find that the global average drops in 2022.

We’ll still rely on fossil fuels for some years to come. But I think in 2022 we’ll see some of the pieces of the zero-carbon future start to come together.

Predictions:

There will be more fusion companies at the end of 2022 than there were at the end of 2021 (65%)
The cost of battery storage will be lower per megawatt-hour at the end of 2022 than it was at the end of 2021(80%)

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Have we seen the end of Covid?

By Tom Chivers

Medicine

The biggest thing in medicine this year has been the Covid vaccines. And they’re pretty astonishing things. Remember that at the start of 2020, there were zero approved mRNA vaccines in the world: now billions of people have been given one, and millions of lives have been saved.

You can use mRNA vaccines for pretty much any disease, and in the next few years we’ll doubtless see them for all sorts of things. One that really interests me is cancer. Cancer “vaccines” aren’t like vaccines for measles or Covid – they’re treatments, given to people who have already been diagnosed with the disease, rather than prophylactics given to people at risk of contracting them. As I understand it, it involves presenting the immune system with the RNA sequence for a protein specific to that patient’s cancer, which will help the body fight that cancer. It’s a sort of personalised advance on existing immunotherapy, which has already turned various previously fatal cancers into largely manageable chronic illnesses. So this could be a really big deal in coming years.

Lots of drug companies have plans to make cancer mRNA vaccines. For instance, BioNTech has four ongoing Phase II trials. I think it’s pretty likely that we’ll hear good news from at least one of them, and that a Phase III trial with thousands of participants will be announced. Let’s say 55%.

Prediction:

A Phase III trial of at least one of BioNTech’s cancer mRNA vaccines will be announced in 2022(55%).

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Football

Until a week or so ago Liverpool were one point off the lead and looking good: then they drew with Spurs, lost bizarrely to Leicester after missing a penalty, and then drawing with Chelsea despite being two-nil up after half an hour. And Man City should have lost to Arsenal but Arsenal decided to be Arsenal and gave away a penalty and had a man sent off within 10 minutes and threw it away.

So now Liverpool are 11 points off the top (albeit with a game in hand) and let’s face it the title race is over in January. I reckon only about a 10% chance of turning it around. Still, there’s always the Champions League.

Prediction: Liverpool will win the league this year (10%)

Conclusion

I wanted to add another couple of questions, but they’re hard to do good data on. For instance, I bet that global extreme poverty will be lower in 2022 than it was in 2021, as it has been almost every year for the last 40. But we won’t get good data for a few years. I also bet that even though the world will get better on most metrics, such as life expectancy, wealth, etc, an increasing share of people will still think that it’s getting worse, because that’s how we work. But that’s hard to get into a straightforward question, and again, even if we could, the data for 2022 won’t be around until 2025 or something anyway. Still, I think those things are probably true.

My final prediction is that these predictions will not be very accurate. I’d love it if you would have a go and see if you can do better.

At one stage he even says: “When speaking to a contemptible idiot who is kind of evil, don’t call them a contemptible idiot who is kind of evil! Many contemptible idiots find that language insulting.” But he continues to do it, and frequently segues into lengthy digressions about how stupid and harmful the idiots’ beliefs are. Presumably you would not feel that the author had really taken his own advice on board

This is very much how I feel about How to Talk to A Science Denier, by the Harvard philosopher Lee McIntyre.

McIntyre wants to help us change people’s minds. Specifically, to help us change the minds of these strange, incomprehensible people called “science deniers”. He addresses five main groups of “deniers”: flat earthers; climate deniers; anti-vaxxers; GMO sceptics; and Covid deniers.

This is, on the face of it, an important project. It’s a truism that the world is polarised, and our sense of shared reality is under attack. If there is some way of learning how to talk across difference, and to persuade without attacking, that might go a long way to bridging our various divides, not just the five he discusses.

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The framing is that McIntyre goes and meets representatives of these groups and tries to persuade them out of their wrong beliefs. He goes armed with social-psychology research about how best to persuade people. His big trick (which I think is a good, if limited, one) is asking: what evidence would it take to make you change your mind?

But the whole book is premised on one idea: McIntyre is right, and the people he is “talking to” are wrong. 

And it’s true that all five groups are wrong, or at least their central claims are. The earth is in fact an oblate spheroid; the climate is warming, due to human influence, and will likely have severe negative impacts; vaccines work; GMOs are safe; and Covid is real.

The trouble is that by using these groups, McIntyre is playing on easy mode. When your example of a “science denier” is a literal flat-earther, it’s easy to say “look over there at the crazy deniers”.

Even with climate change scepticism, sure, there are people who literally don’t believe that anthropogenic greenhouse gases are warming the planet. But those people are relatively rare. People who believe that anthropogenic greenhouse gases are warming the planet, but that the emissions are going to be hard to stop because of economic growth in the developing world and it would make more sense to concentrate on adaptation rather than mitigation, are much more common. Are they “deniers”? Certainly they’re often called deniers. But McIntyre himself acknowledges that China is by far the largest emitter of greenhouse gases and that the IPCC says the sweeping global changes required to cut emissions sufficiently to avoid a 1.5°C warming are unprecedented.

McIntyre constantly wants to make a clean distinction between “science deniers” and non-deniers. So, for instance, he says that there are five “common reasoning errors made by all science deniers” [my emphasis]. They are: cherrypicking, a belief in conspiracy theories, a reliance on fake experts, illogical reasoning and an insistence that science must be perfect. If you don’t make all five of those errors, you’re not an official McIntyre-accredited science denier.

Hang on, though. A “belief in conspiracy theories”? McIntyre spends a lot of time talking about the tobacco firms who manufactured doubt in the smoking/lung cancer link, and the oil firms who did the same with the fossil fuel/climate change link. He says that the spread of Covid denialism through the US government was driven by Republican desire to keep the economy open and win the election. Aren’t these conspiracy theories?

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Ah, but for McIntyre these aren’t conspiracy theories, they’re conspiracies. The distinction is “between actual conspiracies (for which there should be some evidence) and conspiracy theories (which customarily have no credible evidence).”

So, since some anti-vaxx conspiracy theories like the polio vaccine giving children polio, or the CIA using fake vaccination stations to take people’s DNA, are true, does that mean anti-vaxxers don’t believe in “conspiracy theories” but “conspiracies”?

Obviously not. But the point is that there’s not some clear line between “real conspiracies” and “conspiracy theories”. When Alex Jones says that chemicals in the water are turning frogs gay, he’s referring to real claims that endocrine disruptors are affecting sexual development in lots of animals. It’s not easy to draw a line between real and fake, evidence-based and not evidence-based.

I think the basic problem is that McIntyre is a Popperian. That is, in hugely oversimplified terms, he believes that no amount of evidence can confirm a theory: but evidence can falsify it. “If we find only evidence that fits our theory, then it might be true,” he writes. “But if we find any evidence that disconfirms our theory, it must be ruled out.” 

I, on the other hand, am a Bayesian. I have some prior belief and I assign some level of probability to it: “climate change is real and dangerous”: 90%; “the world is flat”, 0.1%. And then each new piece of evidence shifts my belief a little: if next year NASA say “we got new photos in, looks like Earth is sitting on the back of a turtle”, then I’ll upgrade my belief in a flat earth to, I dunno, 1.5% (but also upgrade my belief in there being mad people at NASA to 95%).

So I don’t need to draw a bright line between “denial” and “reality”. I can say: “I think it’s likely that tobacco firms conspired over lung cancer, but I think it’s pretty unlikely that NASA faked the moon landings.” And I can update my beliefs as new evidence comes in. I don’t have to “rule anything out”, I can simply downgrade how likely it is.

McIntyre, though, is stuck with two categories: things that might be true; and things which have been “disconfirmed”. If you believe things that have been disconfirmed, then you must be a “denier”. And so he needs to find ways of explaining why these “deniers” are so different from the rest of us. 

He has various ideas about “inflated self-confidence, narcissism, or low self-esteem”. But if you reject the idea that there are two groups of people, “deniers” and “non-deniers”, then you can avoid the need to explain it at all, beyond saying “some people are better than others at working out what’s true”. 

But we’re not just here for his epistemology: we’re here for a masterclass in how to persuade people out of false beliefs. Over the course of the book he meets various people — the flat earthers; two coal miners; a couple of hippyish friends of his — and tries to talk to them about their beliefs, using the methods he has learnt. His solution is to listen, to be respectful, to meet people face to face, and to do so over several meetings. Does his approach work?

In short: no. Hilariously, both of the coal miners he meets cheerfully accept the reality of climate change, but say that the economic value is worth the potential damage to the climate. His first hippyish friend is entirely pro-vax and only slightly GMO-sceptical; the other one is anti-GMO but on anti-corporate grounds rather than safety ones. 

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So he falls at the first hurdle: he not only doesn’t convince anyone, he doesn’t meet anyone who unambiguously disagrees with him, except the flat earthers. (He also struggles to be respectful, at least in the book itself. There’s an astonishing line on p77 in which he says “When speaking to them, we should remember that it is an insult to use the word ‘anti-vaxxer.’” There are 109 uses of the term “anti-vaxx” in the book. Occasionally he remembers and says how important it is that we listen and pay attention, then he immediately reverts to calling, say, Covid scepticism “ridiculous conspiracy theories and partisan nonsense”.)

But there’s a bigger problem. McIntyre’s big question, as mentioned, is asking: What evidence would it take to change your mind? But at no point does McIntyre ever ask himself what it would take to change his mind. 

For instance: when he was talking to the Pennsylvania coal miners, he accepted that they were just trying to feed their families. I assume he’d also acknowledge that Chinese coal mining is allowing that country to get richer and improve its citizens’ way of life. But I don’t think I’m misrepresenting him when I say that he thinks coal mining is a disaster.

When he talks to a friend of his about GMOs, though, that friend says that even though GMOs can save lives now (in the form of golden rice), they’ll cause disaster in the future. McIntyre says, OK, so the kids who can’t get the golden rice now, they’re just going to die? And his friend says yes. McIntyre says that’s easy for him to say, “because he had money and wouldn’t be one of the ones who suffered”.

The exact same question, though, can be asked about coal mining. Sure, McIntyre can say stop using coal, and it’ll help prevent future disasters. But it will also presumably mean some number of tens or hundreds of millions of Chinese people losing electric lights and functioning hospitals, and a smaller number of Pennsylvanians losing their jobs. McIntyre himself would be fine, except for somewhat higher electricity bills. 

Is the tradeoff worth it? McIntyre clearly thinks so (and I think I do too): but what would change his mind? I can tell you: I would update my beliefs significantly if you showed me a utilitarian calculation showing that more people would be harmed by ending coal mining than by continuing it. But McIntyre never asks himself the question. He is stuck on transmit, never on receive.

What’s sad is that he sometimes comes close. He recognises that beliefs are part of people’s identity, and that that makes it hard to change them – but again, applies the lesson only to the weird, wrong, other people, not to himself and people like him. The near-total lack of introspection renders the whole grand project largely meaningless. I am right, you are wrong, the only thing we need to discuss is how to make you realise how wrong you are. The idea of working together to establish a shared reality is hamstrung by his certainty that the reality that needs to be shared is his one.

It’s mainly a book designed to tell readers that people they already think are dumb are, in fact, dumb. It is, really, How to Talk to A Contemptible Idiot Who Is Kind of Evil.

This piece was originally published in August. 

Caution is a strange word to use. On the day they released the statement, about 70,000 new cases of Covid were confirmed in the United States, and about 1,000 people died of it. Is it “cautious” to stop using a vaccine which would almost certainly reduce those numbers, because of an uncertain chance that it might have negative effects in a tiny cohort?

But caution, in this sense, has been rife during the pandemic. The governments of various European countries stopped the use of the AstraZeneca vaccine over similar concerns. Germany, the Guardian reported in March, “was the first country to refuse to allow people over the age of 65 to have the AstraZeneca vaccine because of the absence of evidence of how well it worked in older people, indicating a more cautious approach than most”. 

Over the last two years, again and again, the fears of some possible risk caused by something we might do have outweighed the fears of a thoroughly real, utterly obvious risk which was killing people at the time. And it has, I think, been a failure of the field, or at least the practice, of bioethics.

Bioethics is the study and application of ethical philosophy as it applies to medical and biological research and practice. As a former philosophy student, I have always found the idea very strange – after years spent arguing over the wildly different implications of minor-seeming differences in ethical frameworks, it was amazing to me that you could corral ethicists into things called “ethical review boards” or “independent ethics committees” which would then give you an answer to the question “so is this ethical or not?”

(“We are pleased to inform you, Herr Doktor Kant, that the board rules that you may lie to the murderer for the purpose of concealing your friend’s whereabouts.”)

Drugs continue to be licenced and trials continue to be carried out, so I suppose not every ethics committee gets bogged down for three thousand years in discussions of what we mean by “the good”. And perhaps in normal times, the system works reasonably well, although you do hear some horror stories.

But during the pandemic, something has gone terribly wrong, and I think that the system of ethical approval in medicine has probably cost tens of thousands of lives, at a conservative estimate.

There’s a tweet from 2018 by a California software engineer, about the ethics of self-driving cars. It goes:

Philosophy: so sometimes it goes haywire & ends up-

Me: do the thing that kills fewer people

Philosophy: but it's very salient th-

Me: do the thing that kills fewer people

Philosophy: but the human element of control is-

Me: do the thing

Me: that kills

Me: fewer people

— Mason (@webdevMason) April 2, 2018

I think about it quite a lot. There are interesting questions, sure: if a self-driving car kills someone, who is responsible, the driver or the manufacturer? If you’re designing a safety system for the self-driving car, should it value the life of the occupants more highly than those of people outside?

But they’re quite niche questions. If, in the future, it turns out that a self-driving car will, on average, kill fewer people than a human-driven car, then it would be the ethical thing to do to get a self-driving car. Even if, every so often, the self-driving car does something really weird, like mistake a cyclist for a road marking, or a lorry for the sky, fewer people will be killed in a world where humans don’t drive and robots do. And that is good, so – all else being equal – we should say that self-driving cars are the ethical choice.

You can construct clever scenarios in which that is not true. But they have to be clever. As a starting point, as a reasonable first draft, “Do the thing that kills fewer people” is hard to beat.

In bioethics, though, we’ve overcomplicated things. For instance, early in the pandemic, people were campaigning for “human challenge trials” into Covid vaccines. In normal vaccine trials, people are given the vaccine (or a placebo or other control), and then the researchers observe how many people get the disease naturally. If it’s significantly fewer in the vaccine group, then we say that the vaccine works.

But it can take months for enough people to catch the disease naturally. When I was on the AstraZeneca trial in summer 2020, prevalence was low – there was real concern that it would take many months to get enough data.

With human challenge trials, participants agree not only to be given the vaccine but also the disease. It lets you use far fewer participants, and get your results far quicker, than a traditional vaccine trial.

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A promising vaccine candidate, the Moderna mRNA vaccine, was ready in a lab in January 2020. The hundred or so doses that would have been required to get very solid evidence of effectiveness could have been made at lab scale in a few days. We could have known by February, or March at the latest, whether the vaccines worked. Yes, we’d still have had to scale up production, and that would have taken months. But the whole process would have started earlier. Instead, Moderna’s vaccine was not given emergency use approval in the US until December.

As the philosopher Richard Yetter Chappell points out, there are obvious-seeming objections to human challenge trials. You have to give people a potentially dangerous disease: what if it kills one of them?

But one plausible estimate is that roughly 18 million people have died of Covid during the pandemic – that’s an average of about 28,000 a day. Bringing the end of the pandemic forward by even a single day could easily save thousands of lives. A small risk to a small number of young, healthy volunteers was hugely outweighed by a very likely large reduction in risk to many thousands of old, vulnerable people.

Philosophy fans might think that this is a classic utilitarianism problem: is it OK to sacrifice one to save many; can I torture the terrorist in order to find the bomb? But as Chappell notes, in fact it is not. There are willing volunteers offering a (small but real) sacrifice for the greater good. It is an act of altruism, or even heroism, not coercion. “In what other context would the default assumption be to ban heroic acts of immense social value?”, asks Chappell.

There have been other failures. Recently, the drug Paxlovid was shown to be highly effective against severe disease. So effective, in fact, that the trial was stopped midway, because it was deemed unethical to give half of the participants a placebo when it was clear the real drug worked.

But the drug is still not approved in the US. So as Zvi Mowshowitz points out, “It is illegal to give this drug to any patients, because it hasn’t been proven safe and effective,” but also, “It is illegal to continue a trial to study the drug, because it has been proven so safe and effective that it isn’t ethical to not give the drug to half the patients.” 

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This is not the only such case. A trial of a drug designed to protect against HIV was stopped last year, because the drug was so effective that it was unethical to give placebo. But the drug was not actually approved by the FDA until … Monday!

There are two important points to make, here. First, while I’m talking about “bioethics”, it’s not clear that it’s actually bioethicists who are the problem. For instance, Peter Singer of Princeton, probably the world’s most famous bioethicist, is on the board of 1DaySooner, the human challenge advocacy group, as is his fellow bioethicist Nir Eyal, of Harvard. Leah Pierson, a Harvard bioethicist who is writing a book about the failings of bioethics during the pandemic, stresses that when the CDC paused the use of the J&J vaccine, lots of the bioethicists she knows were appalled at the decision. But the practice of bioethics as actually carried out in major institutions, such as the FDA and CDC, often leads to these bad decisions. Matt Yglesias makes a good case here that public health agencies tend to follow somewhat rigid rules, rather than the best available science, leading to bad outcomes like a delay in approving fluvoxamine: perhaps I should complain about “institutionalised public health” rather than “bioethics” per se.

Second, as Pierson points out, “When these systems work well you probably don’t hear about it.” No doubt there are lots of drugs that get approved relatively smoothly, and trials which go ahead without much fuss. I don’t know how representative these problems are.

But the problems do exist, and they seem to be exacerbated by the pandemic. Chappell thinks that the main problem is one of status quo bias: that is, that changing things feels like the “risky” option, and keeping things the same feels “safer”. And, he admits, that may (or may not) be true in non-pandemic times. But in the pandemic, the status quo is visibly very dangerous. Throwing some low-but-not-zero-risk options into the mix, like early approval of vaccines or human challenge trials, are almost certainly lower-risk, in terms of the likeliest expected outcomes, than sticking with the status quo. 

There is also an issue that humans instinctively think there’s a difference between bad outcomes caused by our actions, and bad outcomes caused by inaction. It’s hard to make a good philosophical case for this, and what the distinction between an “act” and an “omission” is (Jonathan Bennett had a go), but it’s how we feel. Killing one person by giving them a faulty vaccine feels worse, somehow, than letting a thousand die because we let the vaccines sit in a warehouse for another 24 hours.

And it’s easy to come up with reasons why we need to put more hoops in place for researchers and clinicians to jump through, because the one guy who dies in a botched human challenge trial is very obvious, whereas the thousands of people who would have died if the trial never took place are completely invisible.

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But whatever the reason is that the hoops are in place, they are in place, and people have to jump through them to get things done. It took until December for the UK to decide to vaccinate the under-12s, despite it being well established that schoolchildren were driving the pandemic, because bioethicists could only take into account direct risk to the patient at the time – not the likelihood that prevalence would go up, or whether the children would rather not put their own relatives at risk. And it’s amazing, with hindsight, to read this approving piece from October last year about how a doctor prevented Donald Trump from forcing through early approval of a Covid vaccine.

It’s rare that academic philosophy can have such a direct impact on people’s lives. But how we apply bioethics really can save or kill thousands, just by changing the speed with which we approve drugs. In peacetime, perhaps, it’s OK to argue the toss and act with caution. But in a pandemic, perhaps we really ought to apply the standard of “Do the thing that kills fewer people”.

More ambiguously, last week there were about 6,000 hospital admissions with Covid in South Africa, all of which were presumably Omicron since the variant is now dominant there. That’s well below the July peak of about 15,000 a week, although there’s a lag between infection and hospitalisation.

This sort of news has led to people thinking that it’s much less deadly than Delta. As the Omicron wave looms over Britain, that sort of hope keeps us sane.

But how much can we trust that hope? The Prime Minister has promised to ramp up the booster programme, saying that everybody over 18 would be vaccinated by 31 December – about a million people a day. Can that really be possible? And if it were, would that be enough, or are we going to need something more than the “Plan B” restrictions that are already in place.

First, we need to know if it really is less deadly than the other variants. It’s not as clear-cut as all that. South Africa has only fully vaccinated about a quarter of its population, but it has had three large waves of infection: very probably, by now, most of the population has had the disease. So it’s not possible to directly compare the Omicron wave with the Delta wave: Omicron is working its way through a population with much higher immunity.

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The trouble is, that won’t be true in the UK. When Delta came here, we were already highly vaccinated: almost every adult had had at least one jab. At least some of the reasons why Omicron appears to be less of a threat to South Africans don’t apply here. We’re also a much older population.

Closer to home, Danish data looking at 2,417 cases of Omicron found only 19 people had been admitted to hospital. But again, it’s hard to know what to make of it. The patients involved were mainly young – the median age is about 30. And given an average lag of about a week between infection and hospitalisation, and since about 2,000 of the cases were detected in the last week, it might not be as promising as it sounds on the surface.

The most recent UK Health Security Agency technical briefing similarly finds that “None of the cases is known to have been hospitalised or died”. But their data only goes to 6 December. By 6 December, there had only been 260 cases of Omicron in the country, most of which had been detected in the previous few days. Since then, there have been at least 10 hospitalisations and at least one death linked to the new variant. Things may become clearer in the coming weeks.

It’s perfectly possible that Omicron is less dangerous than Delta to a person with prior immunity. But we can’t be sure yet. And we should also remember that while deadliness — virulence — is important, it’s not as important as transmissibility.

Imagine the following scenario. You have a disease with an R of four — that is, on average, each person with the disease gives it to four more people. And it also has an infection fatality rate (IFR) of 4%, so it kills one person in every 25 that it infects. (These are made-up numbers for illustration.)

You follow that disease through 10 generations. The first person would infect four more; those four would infect a further 16, and so on. By the time you reached the 10th generation, you would have about a million new cases – and adding all the generations together, you’d get a total of 1.4 million cases. Of those 1.4 million, about 56,000 would die.

Imagine that a new variant arrived which was more deadly: which kills 5% of victims. It would be worse. You’d expect about 70,000 deaths, 25% more than before.

But now imagine that a new variant arrived which was more transmissible, by the same margin. Each person infects five people, on average. Suddenly the first one infects five, those five infect 25, those 25 infect 125. By the time you reach the 10th generation, it’s almost 10 million new cases, and about 12 million in total. Even if it still only had an IFR of 4%, it would kill about 480,000 people – nine times as many as the original.

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So even if Omicron is less deadly, if it’s very good at spreading through the population, it could quickly overwhelm the health service just through sheer weight of numbers.

The question, then, is: is it more transmissible? There was much excitement over the weekend, when the case numbers appeared to be slowing there — after zooming up to 19,000 a day, they dropped back to 17,000, and people started to wonder whether it had peaked. But then the South African health service released a new tranche of data on Sunday, with another 37,000 cases — the apparent slowdown appears to have been a reporting backlog.

Even taking that into account, Omicron isn’t doing quite the mad upwards dash that it was in late November. But it’s still escalating, in a presumably fairly immune population. And it seems to be spreading even faster in the UK: Paul Hunter, a professor of medicine at the University of East Anglia, told the Science Media Centre that “In South Africa the latest R estimate is about 2.2. For the UK the estimate is 3.7 which is doubling every 2 to 3 days.” The UK HSA technical report suggests (p19) that it spreads around twice as easily as Delta in the UK population. If that’s true, it would easily overwhelm any gains from reduced virulence, even if they’re real.

So what does it all mean? Over the weekend, the London School of Hygiene and Tropical Medicine released a model of how Omicron might spread. It included scenarios with different assumptions about how good the new variant is at evading immunity, and about how good the boosters are at building that immunity back up. It also assumed that we’d keep the Plan B advice in place (masks in some public spaces, people working from home if they can, some vaccine-status checking) and that the NHS would administer about 500,000 new booster jabs a day.

The most likely scenario is that the variant is pretty good at evading immunity, but that boosters work pretty well. Under that scenario, the LSHTM model’s projections look fairly bleak: a peak of about 6,000 hospital admissions a day, compared to about 4,000 a day at the January 2021 peak. And they think between about 40,000 and 50,000 people would die of Covid between 1 December and 30 April.

They say in the model that they have assumed Omicron is no less deadly than Delta, given prior immunity. They might be wrong about that, but as I’ve shown, we can’t be sure. It’s certainly very possible that we’ll be looking at another wave of deaths comparable to the first or second one.

Will Johnson’s booster boosterism make much of a difference? I doubt it. It takes at least a week for boosters to have a significant effect on your immunity, and for some people more like two. Even if we started vaccinating a million people a day from tomorrow, the impact of that wouldn’t be felt until around Christmas. We’re not going to start vaccinating a million people tomorrow: we’ve been averaging 400,000 a day recently, fewer than the LSHTM model assumes, and the NHS thinks that we’ll still be boosting well into January and February and we’ll only have offered everyone a jab by the new year. Hopefully it’ll ramp up quickly, but if we’re relying on boosters, then most of the January caseload will be baked in already.

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And here’s the really worrying thing. Sajid Javid, the health secretary, told MPs last night that 200,000 people have been infected by Omicron yesterday alone. This was such a huge claim that people assumed he’d got mixed up: perhaps he meant 200,000 people are infected? Or have been? But apparently not. There were about 50,000 cases reported in the UK on 10 December. The ONS infection survey suggests that we only detect 40% of them: that is, there were really 125,000. On the 10th, 19% of cases were Omicron. That is about 25,000 cases. But cases detected on the 10th were probably infected on the 7th, six days ago. And if it’s doubling every two days, then that 25,000 has had time to become 200,000.

For reference, that’s about the number of daily cases that we had in early January 2021, the peak of the second wave, by which time we’d already been in full lockdown for a month. We are not in lockdown now, and several more doublings are probably already locked in. If Javid is correct, then Omicron had better be much less deadly, or there could be real trouble ahead. We’ll know if he’s right in about three days. 

So should we be talking about restrictions?

The restrictions we put in place for the first and second waves were intended to avoid something far worse. The 16 March 2020 paper by Imperial College London predicted 500,000 deaths if no measures were taken. That was what sent us into lockdown. The actual death toll of 40,000 or so in the first wave was a fraction of what it could have been.

But we are nowhere near where we were early last year. We’re now talking about something that is on the level of a very bad flu season. Full-on March 2020 lockdown is probably an overreaction.

But the LSHTM model thinks that we could prevent about two-thirds of those deaths and hospitalisations by going to full lockdown,[1. It actually only models the most optimistic and most pessimistic scenarios under the various restrictions. But in both of those scenarios, about two-thirds of deaths and hospitalisations are prevented under strictest lockdown, and about a third under the less stringent Stage 2 restrictions. I have assumed that the intermediate scenarios see roughly similar numbers.] and about one-third by introducing, right now, some less strict measures, equivalent to Stage 2 of the “roadmap out of lockdown” from spring: the biggest part of that was probably that only two households or six individuals could meet indoors.

It is a non-trivial intervention that could slow the course of Omicron; it might buy some time for the sped-up booster programme; and it would mean we wouldn’t have to cross our fingers and hope that Omicron is less deadly than Delta. Obviously politicians will be reluctant to ruin another Christmas. But relying on boosters alone is a big risk, especially since no one but Johnson seems to think we can hit the target.

There was backlash from religious groups, calling the move “eugenics”. But was it? The word “eugenics” can be used to refer to things like mitochondrial donation, insofar as it means something like “efforts to remove ‘harmful’ genes from the gene pool”. But the classic example of eugenics are things like the Nazi programme of forced sterilisation; a programme that did awful things to people against their will.

Using the term “eugenics” to refer to both was, I think, a semi-deliberate tactic. Opponents of mitochondrial donation were trying to associate those evil things in people’s minds with the more complex cases. That doesn’t mean that mitochondrial donation is automatically right. But the fact that a loose definition of the word “eugenics” can be used to apply to both it and Nazi murders does not make it necessarily wrong.

A similar sort of tactic appears to being deployed in the ongoing debate over the Government’s proposed legislation to ban conversion therapy. 

It was not that many years ago that homosexuality was viewed as a mental illness requiring curing: the Diagnostic and Statistical Manual of Mental Disorders (DSM) listed same-sex attraction as a psychiatric disorder until 1973. And as I’ve written before, there’s not much evidence that you can change someone’s sexual orientation, while the methods used to do it have often been brutal: zapping people with electric shocks while showing them homoerotic images; chemical castration; even lobotomy, as recently as 1969.

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“Conversion therapy” is, therefore, rightly held up as something unacceptable: the classic example of conversion therapy is a gay man or lesbian woman being forced, against his or her will, to submit to something approaching torture, in an almost certainly unsuccessful attempt to change his or her sexual orientation.

The Government’s legislation, though, refers not just to efforts to change a person’s sexual orientation, but also to efforts to change their gender identity. And I’m concerned that, just as with the word “eugenics”, using the term “conversion therapy” like this stretches it to beyond the point of usefulness.

Of course if someone is trans, they should not be told that they are not — or told that they are inferior, or defective. But sexual orientation is a fairly stable and well-defined concept: we know what is meant by being sexually attracted to one sex or another or both, and, on the whole, people’s sexual orientation remains the same throughout their adult lives. Importantly, also, if someone does change their sexual orientation, all they need to do is start dating people of the appropriate sex.

That’s not true of gender identity. While there clearly are many people who don’t identify as their biological sex, exactly what is meant by a “gender identity” is unclear. The Human Rights Campaign defines it as “one’s innermost concept of self as male, female, a blend of both or neither”. How one feels is important, but it is subjective — and unlike sexual attraction it’s not tied to obvious behaviours, such as choice of sexual partner.

It’s also not clear that it’s stable. While some young people are clear from a very young age that they do not identify as the sex they were born in, and remain so until adulthood, that’s not true of everyone. According to several medical textbooks, perhaps as many as 85% to 90% of prepubescent children who are diagnosed with gender dysphoria — that is, severe discomfort in their birth sex — no longer do by adolescence, although they do say that if those children are still dysphoric in early puberty, it is “almost certainly permanent”.

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And, crucially, the decision about whether a young person is trans or not can be much more consequential and irreversible than the decision about whether they’re gay or not. Full transitioning may involve hormones, drugs to delay puberty, and surgery.

All of which may be absolutely necessary and right; there are plenty of examples of young people who transitioned and became much happier and healthier. But the combination of an unstable concept and a major, consequential life decision means that we need to be careful about deciding whether someone, especially a young person, is “really” trans or not.

That’s why we should be concerned about the use of “conversion therapy” as an umbrella term. Of course, if there are therapists telling trans people that being trans is shameful or sinful, or that it is a defect that needs to be purged, then that is wrong, and those people should be punished. But I don’t think it’s very common.

The Government’s own research refers to two main kinds of conversion therapy: spiritual, “pray the gay away” versions, and “psychological methods” such as talking therapies. About 5% of lesbian, gay and bisexual people in the UK have been offered conversion therapy, mainly of the spiritual kind, usually because their sexual orientation conflicts with their community’s religious beliefs.

There is much less research into how common gender identify conversion therapy is. But the Government’s assessment does say that “a much higher percentage of transgender respondents (29%) than cisgender respondents (15%) said their therapy had been conducted by healthcare professionals”.

That would make sense, if some therapists are assessing that some patients are mistaken about their transgender status, and offering them alternative routes — for instance, watching and waiting, or treating some mental health condition that they have — and patients are unhappy at the lack of affirmation of their condition. And often such assessments are reasonable: gender dysphoria is often found alongside other conditions, such as autism. There are also other conditions, such as borderline personality disorder or dissociative identity disorder, which can lead to patients having unstable senses of self. While it’s important to listen to patients, that doesn’t mean always assuming that they’re right in their self-diagnosis. 

The Government’s proposed legislation does make room for therapists to “support a person who is questioning if they are LGBT”. But as one GP says, “lots of my patients are not questioning; they are very assertive that they are trans”. Sometimes simply affirming a patient’s, especially a child’s, beliefs about his or her identity, is not the right thing to do.

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Perhaps the safeguards in the proposed law will protect doctors and therapists who want to be more cautious about affirming patients’ gender identity in every case. But some therapists are clearly concerned: the GP quoted above goes on to say that “The way this is worded at the moment, I could end up in prison.” Surely it will not come to that, but it would be deeply concerning if there was a chilling effect on medical professionals’ willingness to talk about gender to their patients.

Like the term “eugenics”, the term “conversion therapy” is powerful: it summons up images of torture, of homophobia, of religious and institutional abuse. And just as with eugenics, that power needs to be directed carefully. The arguments should never be “because we use the same term to describe them as we used to describe Nazi atrocities”. 

Of course, there may sometimes be excellent reasons to affirm children’s gender identities, rather than challenge them. I suspect it may be best to let clinicians use their best judgment, in some cases. But we should be careful about using the term “conversion therapy”. Something has gone wrong if doctors are worried about going to prison for discussing the best treatment for their patients.

But pharma bad behaviour is not new. For example: there’s a thing in patent law called “evergreening”. It’s most famously used by big pharma companies who don’t want their expensive drugs to reach the end of their 20-year patent and become available as a generic, so they develop a very slightly different version of the same drug and get a new patent on that.

Venlafaxine is an antidepressant, marketed as Effexor. As it neared the end of its patent, the manufacturer developed a new version – desvenlafaxine, marketed as Pristiq. Desvenlafaxine is what the body naturally breaks venlafaxine down into; your liver takes the venlafaxine and metabolises it into desvenlafaxine. It is also either less effective or no more effective than the original.

The patent for Effexor expired in December 2008; Pristiq entered the market in early 2009. By 2014, Pristiq was the second most prescribed antidepressant in the US, despite being “a slightly worse version of an older antidepressant with no proven advantages that also costs fifteen times as much”. (A month’s supply of Effexor at the time cost $20; a month’s supply of Pristiq cost $300.)

And it’s not the only bad thing they do. When a drug reaches the end of its patent, other companies can make generic versions. To encourage that, the US FDA says that the first company to do so gets 180 days of exclusivity, so it can establish itself in the market. “Until recently,” says Dr Vishal Gulati, a venture capitalist specialising in healthcare, “it was legal for the original company to pay off the company to not launch the product. Companies would get a license to make the generic, and then be paid to not launch it.” They were literally paying to prevent patients from getting cheaper healthcare.

What’s going wrong here? There’s a very tempting explanation, which is that big pharma is evil. But I never find that satisfying as an explanation. Instead, I’d rather think about incentive structures.

There’s an inherent tension at the heart of any knowledge-based business — anything which develops new technology or information and sells it. You want it to do two things: to create that knowledge, to advance our understanding; and to spread it around, so that the world can benefit from it. Information is a “non-rival good”: if I benefit from it, that doesn’t stop you from benefiting from it too. An example is a lighthouse. If I see the beam, and avoid the rocks, it doesn’t stop you from doing the same. By contrast, a hamburger is a “rival good”: if I eat it, you can’t.

People can make money selling hamburgers. But it’s much harder to do so by operating a lighthouse.

The trouble with knowledge-based businesses is that they are more like lighthouses than like burgers. If you create a new piece of software, or a new technology — or a piece of investigative journalism — then there’s nothing stopping me from simply copying it. And then you won’t make any money off it. That makes it less likely that you’ll spend the time and effort to create any more new things.

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So we created intellectual property laws in order to stop that. Creators of a thing can get a patent or a copyright, and are granted exclusive rights to produce that thing for some number of years — 20 years, in the case of pharmaceuticals.

Problem solved, right? Well, obviously not. Because we don’t just want a world in which new things are created. We also want a world in which everyone can gain access to those things. Intellectual property rights prevent us from making extremely cheap copies of things that we already know how to make. If a foreign correspondent reports human rights abuses in Yemen, it’s best for society if as many people as possible to read that report, so that we can act on it. But intellectual property rights (and their downstream effect, paywalls in journalism) stop that from happening.

This is a direct trade-off. “My mental image of all this is we’re basically squeezing a balloon,” says Owen Barder, a development economist. You can squeeze the top, and move the problem to the bottom. Or you can squeeze the bottom, and move the problem to the top. But the balloon is still there.

And this isn’t the whole problem. We want pharma companies to develop drugs and vaccines for the developing world. But people in the developing world can’t pay as much money as people in the rich world, obviously.

Pharma R&D is expensive: sometimes billions of dollars to research one drug. And for every drug that is successful, there might be 20 that aren’t, and the research into those needs to be paid for as well. That money needs to come from somewhere. At the moment, it comes from patients in rich countries paying sometimes hundreds of dollars for pills that might, individually, cost a few cents to make. The “marginal cost” of each dose is tiny — Jacobin and Oxfam fume that the Covid vaccines, for instance, are priced at many times the manufacturing cost — but that cost needs to cover the “fixed cost” of all the R&D (and marketing, staff costs, etc) you’ve put in.

The ideal solution to this would be selling the drug to everyone at the maximum cost they’re willing to pay. Charge hundreds of dollars in the US, a bit less in the UK and EU, much less in Bulgaria, and almost nothing in Malawi. “If you could perfectly price-discriminate, you’d be charging above the marginal cost in lots of countries, and at the marginal cost for the ones who couldn’t afford more than that,” says Barder.

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But that’s not possible. For one thing, if they tried it, people would buy the pills in Malawi for a few cents and fly to the US and sell them at a profit. But more important, Congress or Parliament (and the press) would kick off – why are our citizens paying hundreds of times over the odds? It would be politically impossible to do. So, instead, they make the drug at a single price, which only rich countries can pay, until the sales in those countries have paid off the R&D costs. “Drug companies are just responding to the incentives we’ve set,” says Barder. “They’re doing the thing we asked them to do.”

The temptation, when faced with problems like these, is to argue that big pharma should be destroyed, and all drug research run by publicly funded university laboratories. And maybe that would work – but it’s a huge gamble. Pharma companies do some bad things, but they objectively do make drugs that are hugely valuable to society. And it’s not that publicly funded bodies are free of bad incentives. Academica has huge problems of its own — academics are rewarded for publishing lots of papers, rather than for necessarily finding out true things. Government’s incentives are to remain popular, rather than to fund the most effective things: it would be easy to imagine lots of money going to fund treatments for picturesque children with cancer, rather than for, say, diabetes, even if it were a much less effective way of saving lives.

Still, it might work. At the moment, as Barder says, there’s a tendency to “socialise the losses and privatise the gains”: private companies get rich off research that is often begun in university departments. A starting point might be to pump lots of money into university research, let them bring drugs to market, and see if they can outperform big pharma. What would be crazy, though, is to destroy big pharma first, and then hope that our new nationalised version can keep new drugs coming through.

A more low-key version, says Gulati, might be for academic institutions to become better at demanding equity in pharmaceutical products that are based on their early research. He also suggests that countries such as the UK could negotiate cheaper drugs by offering the NHS as a source of clinical trial subjects — as has happened with Novartis’s new cholesterol drug inclisiran, aka Leqvio. That’s hugely valuable to pharma companies, and it’s something the NHS can do easily and safely, with its huge, centralised, well-protected data systems.

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Those ideas might help make drugs cheaper in the UK and other rich countries: getting them to poorer countries is a different problem, with different solutions. Governments could buy out patents — if a firm thinks it can make $10 billion over the next 10 years for its product, we could say that we’ll give them the $10 billion now (or a bit less) in exchange for the rights to make the drug available at cost.

Barder likes one idea, put forward by the late economist Jean Olson Lanjouw. “Her suggestion was,” says Barder, “that if I’m AstraZeneca and I show up at the UK patent office asking for IP protection for a new drug, the patent office should say ‘Well done. Can you tell me what you expect the market value for this drug to be in all 200 countries in the world?’”

Then AstraZeneca or whoever would say “I expect most of my revenue to come from the US, UK, EU and Japan, and relatively little from sub-Saharan Africa and Bangladesh.” And the patent office would grant them a patent, on the condition that they license it for free use in those countries that make up the bottom 2% of their revenue. “It’s like a tax of 2%,” says Barder, “but those countries might well make up 80% of the disease burden.” It hasn’t been tried, as far as I know, but it’s worth thinking about, and it would avoid the problems of Congress or Parliament demanding that the drug be made cheap here — although it would only work for global diseases that affect the rich world as well as the poor — diseases like cancer, or heart disease, or hypertension. It wouldn’t help incentivise research into diseases like malaria or dengue, which have no impact on the rich world. The “advanced market commitment”, which I discussed here, and involves Western governments promising to give pharma companies a bonus for every dose bought by developing nations, might be more effective for that.

Andrey Zarur, the CEO of the biotech firm GreenLight[1.  I have done several pieces of paid writing for GreenLight over the last two years], who are producing their own mRNA vaccine for Covid at the moment, comes at the whole thing from a different angle“Pfizer was not designed to make low-cost therapeutics available to every corner of the world,” he says: it’s a 150-year-old company with settled investors and a particular way of working.

He compares it to Apple. “You have a $1,000 iPhone 13,” he says. “Who’s that designed for? My children. Idiot teenagers with rich parents.” Poorer countries need smartphones too, but the solution is not to force Apple to sell smartphones to Ethiopia at a discount. “What you need is an innovative company with different processes.” Instead of demanding changes from 150-year-old companies that are very good at the specific things they do, create smaller, newer companies which do the thing you want. “There’s six billion people in the developing world,” says Zarur. “You should be able to figure out a way to turn a reasonable profit with reasonably priced drugs.”

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It’s obviously true that pharma companies have done bad things. It’s also obviously true that they’ve done marvellous things — I have relatives who are alive today who wouldn’t be without the pharmaceutical industry’s products.

Perhaps there are other systems which could have produced those drugs, other than the undeniably cutthroat capitalist system we have today. But any system would have bad incentives and obvious, easy-to-publicise disasters.

The job of government in this situation should be to find the bad incentives, the market failures, and to patch them; to make it work, to decide which end of the balloon to squeeze. The Indian government, for example, decided a few years ago that it would not accept evergreening any more — it passed a law saying that it would only offer new patents on drugs that were sufficiently different from existing ones.

In 2013, the government won a court case against Novartis, which had tried to get a new patent on a cancer drug, imatinib mesylate — a crystalline version of an existing cancer drug, imatinib. The government said that the new version was no better than the existing one and was simply an attempt to squeeze more money out of the healthcare system.

It worked. You could argue (as Novartis did) that it will reduce innovation; but the point is you can change the system, change the incentives, encourage the behaviour you want, without smashing the system entirely.

Omicron has been detected — and may have first arisen — in Gauteng province, South Africa. There have only been a few hundred cases detected, so it’s hard to say much about it with any real certainty. But it appears to be more transmissible and there are concerns that immunity, whether from vaccination or prior infection, will be less effective against it. There have been, as I write, three cases detected in the UK, and there are real (and justified) fears that we are heading once again into a lockdown Christmas. 

But nonetheless, we have been very lucky.

We mustn’t forget that the virus is constantly mutating. Every time it copies itself, there is a chance that it will make some minor error. Most of those mutations either have no effect or make it less effective at spreading. But every so often one will improve it: perhaps make it harder for the immune system to see, or make the virus quicker at copying itself.

The other new variants, notably Alpha and Delta, had several mutations, but Omicron has dozens: around 50. And 10 of them are on the bits of the “spike” protein on the virus’s surface which bind with our cells — the “receptor binding domains”. Sir Jeremy Farrar, the director of the Wellcome Trust and former member of SAGE, tells me that these are exactly the kind of mutations which virologists have been expecting and dreading: “It’s biologically plausible that they will drive higher transmission. They’ve been identified before as ones we should be really careful about.”

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So far, it doesn’t sound very lucky. But in one mutation, Omicron has been kind to us. Like Alpha before it, it has a particular mutation, 69-70del, two missing RNA letters in its genome — part of the “S gene” which codes for the spike protein. By happy chance, those missing letters are at one of three places that many PCR tests look at.

What it means is that if a PCR test comes back positive on two out of its three areas — if it has an “S-gene dropout” — then you can be pretty sure you’ve got one of the variants. It means that Alpha last year and now Omicron can be tracked pretty effectively, just with PCR testing: even without sequencing the genomes, you can see where these S-gene dropout results are happening and it’ll give you a good idea of how the variant is spreading.

“It’s a complete gift that the S-gene dropout hits this variant,” says Ewan Birney, the deputy director-general of the European Molecular Biology Laboratory. If it didn’t, we would have much less of an idea where Omicron is. (We’ve actually been lucky twice. If Delta hadn’t replaced Alpha, then the S-gene dropout wouldn’t have told us anything, because both the circulating variants would have had it.)

So far, so good. But is the new variant more transmissible and/or more able to bypass our immunity, whether from vaccines or infection? And is it more or less dangerous to people who catch it?

These are hard questions to answer, because at the moment, we only have data from South Africa, and South Africa’s population is tricky to compare with ours. It’s younger, for a start — the median South African is 26-years old, while the median Brit is 40. It’s also much less vaccinated: somewhere around 75% of Britons have been double-jabbed, while barely a quarter have in South Africa (which is, to my surprise, more to do with uptake than supply). It has also had far fewer confirmed cases per capita.

That said, we can make some guesses. When a new variant emerges, it’s difficult to tell whether it has spread because it is better at spreading, or because it happens to have arisen in a convenient place. Imagine a virus which happened to mutate into a new variant in someone who was about to go to a major conference. It might infect hundreds of people and show up all over the country, but it wouldn’t be because it was any more transmissible — it was just that it got lucky.

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With Omicron, this seems unlikely to be the case. Thanks to the S-gene dropout, we can see that it’s spreading faster than you’d expect. Farrar says that “there has been a clear upswing of transmission in Gauteng, which seems to be driven by the variant”. “It probably can outcompete Delta,” says Babak Javid, an immunology lab director at the University of California San Francisco, “although by how much is still up in the air.” 

Is that because it’s better at spreading in general, or because it can evade immunity? So far, we don’t have the data to say for sure. Soon, we’ll start to get results from laboratory studies, and later from epidemiological observations, but Rupert Beale, leader of the Cell Biology of Infection lab at the Crick Institute, thinks it’s “a few weeks before we get useful information, and the other side of Christmas before we know anything definitive”.

But on the plus side, antibodies aren’t the only part of our immune system. We also have T-cells, which are less easily fooled by mutations. To oversimplify, antibodies stop you getting infected in the first place, but T-cells stop you from getting very ill. So Javid and Birney think it is plausible that Omicron will spread quite easily among vaccinated and/or previously infected people, but will be less likely to kill them or make them severely ill.

There has also been speculation that Omicron is inherently less deadly to people with no pre-existing immunity. That’s not impossible, but everyone I spoke to said that any definitive conclusions are premature. “The speculation is extremely uninformed,” says Beale. “We just don’t know until it gets into a more vulnerable population, which sadly it seems it’s likely to. We can hope it’s less pathogenic, but it’s foolish to make that assumption.”

So it might be better at spreading, and able to escape our immunity to at least some degree; and while it’s theoretically possible it might be less deadly, that’s certainly not a proposition we want to put any weight on. The question is: what should we do, and are we facing a second bleak Covid winter?

The pandemic should have taught us that acting early is usually better than acting late, and that preparing for worst-case scenarios is important. It’s like chess, says Beale: “You play your moves in the most flexible order.” If we assume it’ll all be OK, we might be right, but if we’re wrong, we’ll end up with Omicron spreading all over the place. Whereas if we assume it’ll be pretty bad, we can always open up again later on.

I’ve seen a lot of people, some of them very sensible, suggesting that there’s no point closing borders to South Africa because variants will get through anyway, and that it’s just punishing countries for doing good surveillance. But I think they are wrong. 

Yes, the variant will get through, if it hasn’t already. Farrar points out that it was first identified on 11 November, and its sequence suggests that it had already been circulating for some time before then. “I think it may already be on every continent,” he says. But that doesn’t mean that there’s no point in trying to reduce its spread. “Border closures might buy you a little time.” Birney agrees: “Shutting the borders until we understand it, minimising the seeding events, is a good rational move.”

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It’s true that there’s a risk of punishing countries for responsible surveillance, but I don’t see what the alternative is — pretending that they don’t have lots of Omicron circulating? Perhaps the thing to do is also put restrictions on countries with poor surveillance.

Because “buying a little time” might be important. If nothing else, it gives us time to understand it: if Omicron is highly virulent and vaccine-resistant, then our best response will be rather different to what it would be if it isn’t. It also gives us a chance to get more boosters in arms and to stockpile antiviral drugs. 

All the same arguments apply to other non-pharmaceutical interventions, such as introducing vaccine passports or asking people to work from home if possible. But there are other things Britain can do, too. 

One thing is to hugely increase our vaccine manufacturing capacity — something which would be good for us and for the world. Rolling out children’s vaccines and booster jabs faster wouldn’t hurt. One scientist I spoke to suggested making antiviral meds available by post when people get a positive result: “They need to be given quickly to have a good effect, so you can’t rely on GP appointments. You need to link them to Track & Trace”. Cheaper, quicker PCR testing at airports would help.”

But while those measures are important, they’re not addressing the root of the issue. For Farrar, the emergence of Omicron is just another reminder that we need to vaccinate the world, not just the rich world. “There’s been political drift for months now, in terms of access to vaccines,” he said. “The rich world thought it was over, so they’ve been kicking it into the long grass.” Variants like Omicron will continue to arise until the world has a decent level of immunity.

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We’ve been lucky, with Omicron. But it may well still end up becoming the dominant strain, just as Delta did before it. If it doesn’t, then another one will come along. And it will be “incredibly disruptive”, says Farrar: “Everyone’s putting restrictions in.” Certainly, to avoid another lockdown Christmas, we need to act now.

But if we want to be sure we don’t have another one next year, we need to get this pandemic behind us — and that means billions more doses of vaccine, distributed around the world. We won’t always be so lucky.

You can even be treated for it. You can get specialist treatment at a dedicated NHS clinic. South Korea has gaming “rehab centres”. Gaming addicts have “lost interest in their own lives” and “​​do not feel the passing of time in the real world”, according to a doctor who treats the condition there.

But it is far from clear that “gaming disorder” or gaming addiction exists, at least as a well-defined condition separate from any other compulsive behaviour; and there is a hint that the WHO has made the decision under political pressure from China and other countries. 

The WHO says that its decision was based on “reviews of available evidence and reflects a consensus of experts from different disciplines”. But when you look at WHO-commissioned evidence, the studies are completely wild. This review of the literature carried out on behalf of the WHO found that “the prevalence of IGD ranged from 0.21-57.5% in general populations”. This one was rather less crazy, but the studies it was aggregating found that between 0.16% and 14% of people had the disease. Another found 0.7% to 25%.

For comparison, about 8% of people who take opioids in the US end up addicted. So video games might, if we take those numbers at face value, be several times as addictive as opioid painkillers, which seems… unexpected. Or, equally, it could barely exist at all.

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“The problem,” says Dr Pete Etchells, a psychologist at Bath Spa University and author of Lost in a Good Game, “is that depending on your definition, your understanding of who has or doesn’t have this disease varies wildly in the literature”. That is because, he says, “we don’t know what it looks like, we don’t know what it is, and we don’t know what its unique features are that separate it from other behavioural or impulse disorders”.

Obviously, some people have problems with playing video games too much. You will have read stories about South Korean teenagers wetting themselves rather than getting up from their gaming chair, or people developing blood clots. But rare anecdotes don’t tell us much about the wider problem, and people can develop problematic relationships with almost every form of enjoyable human activity — with exercise, with sex, with tanning.

The question is whether there is something unique to gaming which causes these problems. Dr Andy Przybylski, a psychologist at the Oxford Internet Institute, has worked on gaming addiction in the past, and argues that — as far as we know — there isn’t. He carried out a study in 2017, which looked at people who were classified as “addicted” to gaming at one time, and checked whether they still were six months later. If gaming “addiction” was comparable to, say, tobacco, gambling, or alcohol addiction, then you’d expect that most people would be. 

But as it turned out, of the 6,000 people recruited, none of them met the diagnostic criteria for gaming disorder at both the beginning and the end of the study. That is, no one stayed “addicted” for six months. Dr Netta Weinstein, another author of the study, told me at the time that it’s “a question of whether a diagnosis is stable”, and it suggests that internet gaming probably isn’t an addiction like smoking or alcohol.

Przybylski, then, was surprised to see that the WHO decided to classify IGD as a separate illness, and has been asking the WHO whether or not they have any more evidence. Recently he received an email which said: “It is challenging, if not impossible, to document and communicate through WHO channels the rationale and justification for each decision.” 

But obviously you can prove, or at least provide strong and convincing evidence for, the existence of most illnesses, and the WHO could very straightforwardly point to that evidence. There’s a reason why Covid denialists are considered crackpots and cranks: because it’s pretty straightforward to develop diagnostic tests which show you the presence of a virus, and you can tell that the presence of that virus correlates strongly with a particular set of negative health outcomes.

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With psychiatric conditions, of course, the picture is often messier. You can’t swab someone and see if they have depression; you can only ask them a series of questions, or observe their behaviour. But there are established criteria by which to do so, and when you test someone with one twice, a week apart, they usually give the same answer.

But with gaming disorder, as we’ve seen, that doesn’t seem to be the case. So the WHO creating a new diagnostic category is a big deal. It gives clinicians licence to treat the disorder, and — perhaps more importantly — it tells people, and parents, that gaming disorder is a real thing. “It’s a very emotive topic,” says Etchells. “If you say suddenly that games can be addictive, so many people play them that that can be a really scary thing. We already know that parents are scared and concerned. Throwing it out there without any explanation or caveating, I feel it’s quite irresponsible.” He worries that the WHO decision will pathologise normal, healthy behaviour, like playing video games after work to destress.

The question, then, is why has the WHO done it? They didn’t need to; the American Psychiatric Association hasn’t yet added it to the Diagnostic and Statistical Manual of Mental Disorders, and the Royal College of Psychiatrists hasn’t formally recognised it. 

One possible answer is that the WHO has been pressured into doing it. Professor Geoffrey Reed, a medical psychologist at Columbia University and senior project officer for the WHO’s ICD-11, told another psychologist by email in 2016 that the WHO was “under enormous pressure, especially from Asian countries” to include IGD.

(I’ve asked both Professor Reed and the WHO about this; the WHO has declined to comment at short notice, and if Prof Reed gets back to me, I’ll include any response here.)

There has been huge concern about video gaming in several east Asian countries. In Japan and South Korea, there have been years of worries about the “hikikomori”, young adults who shut themselves off from society, living in their parents’ homes, never leaving, eating delivery food, watching Netflix, browsing the internet and playing games. The phenomenon has also been widely reported in China, Hong Kong and Singapore. These countries are huge consumers and producers of video games, and notably of spectator e-sports, and people have been quick to blame video games for the condition.

And this has led to a widespread reaction which looks suspiciously like a moral panic. South Korea banned under-16s from playing internet games between the hours of midnight and 6am in 2011 to improve children’s sleep, a decision that was only overturned in August. Etchells says that research showed the ban was counterproductive — it increased children’s time on the internet and “had no meaningful effect on increasing sleep”. China recently enacted an even more stringent law, banning under-18s from using internet games between 10pm and 8am. 

Societies are entitled to ban anything they want, of course. But the concern is that they’re hiding behind science to do it. “It’s an extreme example of people pathologising things they find distasteful,” says Dr Stuart Ritchie, a psychologist at King’s College London. “Some people find video games distasteful — they don’t like the idea of kids shooting at each other. But you have to ask what the quality of the evidence is.” Przybylski agrees: “If people want to create rules, they should create rules. But if you’re saying it’s based on evidence or science, you should show your notes.”

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“We’re talking about very complex generational issues, and trying to explain them by looking at one simple factor, and that’s never the case,” says Etchells. If there was a simple causal link, he points out, given the billions of users, you’d expect to see enormous effects, not weird ambiguous trends in messy data.

The trouble is, as Przybylski says, that mental health provision is poorly resourced and expensive. If a teenager is diagnosed with a mental health condition in the UK, he says, “they can age out of being a teenager before you’re seen by a psychiatrist”. Video game addiction, on the other hand, is shiny and exciting, and it sounds cheap, because it seems like there’s an off switch — just turn off the console!

But it comes at a cost. For one thing, even if hikikomori is a real problem in China and other countries, and even if a causal link can be shown to video games, it makes no sense to create a global diagnosis for a highly region-specific problem. For another, it frightens gamers and their parents, perhaps unnecessarily, and gives cover to any old quack or charlatan who wants to promise to treat “gaming addiction” at their expensive clinic, despite there being no clear diagnostic criteria and no agreed treatment.

Most of all, though, there’s a reputational risk for the WHO. “It’s putting its credibility on the line,” says Przybylski. It’s supposed to be a neutral scientific body: it cannot be seen to be making scientific decisions for political reasons. For the last two years, it’s faced criticism of cosying up to China over Covid – praising the Chinese government for transparency and for “setting a new standard for outbreak response” even as it censured doctors for trying to spread the word about the disease. If it transpires that the WHO has put gaming disorder into the ICD-11 as a result of political pressure, whether from China or elsewhere, its credibility will be even more undermined.

The academic community and the WHO have “really dropped the ball” on gaming disorder, says Etchells, rather than being brave enough to stop, take stock, and work out whether it really exists at all as a coherent concept. “I can see how it’s difficult for the WHO to go against these strong opinions, but they need to,” he says. “They can’t come up with disease classifications built on politics.”

The puzzle that Gödel was describing would come to be known as P vs NP. To oversimplify, it asks: can every mathematical question which can be checked quickly also be solved quickly? For instance: you are given a half-complete Sudoku puzzle. Is there a legal solution? If someone were to show you a solution, you could quickly verify whether it was legal. If you used a larger grid, the solution would take longer to check, but not exponentially so[1.  In fact the length of time would grow polynomially – hence P.].

But establishing that there is a legal solution is much slower. There might be quintillions of possible ways of filling it out; the number grows exponentially with the size of the grid. Checking them all one by one might take millions of years even on a powerful computer, if the grid is large enough.

What Gödel wanted to know was: is there some algorithm that could solve the Sudoku (or similar problems) as quickly as we could check a solution? P vs NP is one of the great outstanding questions of mathematics: it has profound implications, but no one has been able to prove it, one way or the other.

The Man from the Future, Ananyo Bhattacharya’s fascinating, fast-moving intellectual biography of von Neumann, made me think of P vs NP. Not because von Neumann solved it; but because von Neumann, in Bhattacharya’s telling, provided solutions to many other previously unsolved problems, in dozens of different fields; others simply had to check them, and expand on them. There is, I think, some discomfort about calling people “geniuses” these days, or in admitting that intelligence is a real thing or that it shapes history – but von Neumann was a genius, and his extraordinary intelligence shaped the modern world.

He was not an economist, but he developed the use of fixed-point theorems in economics in a paper which the historian Roy Weintraub calls “the single most important article in mathematical economics”, and which inspired “half a dozen” Nobel laureates.

His work on game theory – he invented the field, and coined the term “zero-sum game” – inspired at least half a dozen more. Game theory also transformed the study of evolution, inspiring the work of Bill Hamilton, John Maynard Smith, and Richard Dawkins.

He developed utility theory, the basis of modern economics. In 2011 Daniel Kahneman, another economics Nobel laureate (who won his Nobel partly for building on von Neumann’s game-theory ideas), called it“ the most important theory in the social sciences”.

Some of his last work, with Stanislaw Ulam on “cellular automata” – grids of squares that turn on and off according to simple rules – shaped modern computer science in thousands of ways, notably inspiring John McCarthy, who would go on to coin the term “artificial intelligence”.

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Von Neumann’s genius was apparent early. In 1915, at the age of 11, he had gone to the famous gymnasium school in his native Budapest; the “legendary” maths teacher, László Rátz, immediately realised that von Neumann was beyond his ability to teach, and sent him for extra tuition at the local university. There he was mentored by Gábor Szegö, later head of Stanford’s maths department, who was “moved to tears” by his brilliance.

At 17, still at high school, he partly rescued Cantor’s set theory, the basis of much mathematical theory, from a crippling paradox. A couple of years later, he helped reconcile Werner Heisenberg and Erwin Schrödinger’s rival models of quantum mechanics. In the early Thirties, he met the astronomer Subrahmanyan Chandrasekhar, and worked with him on general relativity and the behaviour of stellar clusters. Chandrasekhar would later tell an interviewer, “If I say, ‘He reminds me of von Neumann,’ that’s about the best compliment I can give anyone.”

Von Neumann read some Alan Turing research which imagined a hypothetical computing machine, and saw how to build a working computer. The paper he produced building on Turing’s ideas is considered “the birth certificate of modern computers”, according to the computer scientist Wolfgang Coy. With his wife Kläri, and Ulam, he pioneered Monte Carlo simulations, vital now in climate modelling and a million other fields.

In almost every sphere of scientific inquiry – physics, biology, maths, economics, the social sciences, computing – you find von Neumann’s fingerprints. There is a Wikipedia page of “List of things named after John von Neumann.” Were it not for him, our understanding of the world would be decades behind where it is.

What created this genius? Bhattacharya does not speculate a great deal, but there are things worth considering. First, simple genetics: his family was high-achieving. His father was a doctor of law and an economic adviser to the Hungarian government; his uneducated maternal grandfather apparently could “add or multiply numbers into the millions” in his head instantly, a trick von Neumann emulated. The family was “puzzled” by their son’s inability to play the piano properly at the age of five, suggesting rather higher expectations than most. But it turned out to be because he “had taken to propping up books on his music stand so he could read while ‘practising’”.

He also grew up in a fertile environment. Around the turn of the 20th century, the Budapest Jewish community of which he was part produced an astonishing number of great thinkers. Near-contemporaries included Dennis Gabor, “who won the Nobel Prize in physics in 1971 for inventing the hologram”; Theodore von Kármán, after whom the “Kármán line” is named, denoting the boundary between the Earth’s atmosphere and space; and Eugene Wigner, Edward Teller, and Leo Szilard, three of the greatest minds behind the Manhattan Project. The atomic bomb has been described as a “Hungarian high school science fair project”.

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The Hungarians who worked on America’s atomic weapons programme in the Thirties and Forties were known as “the Martians” by the other physicists – the joke being that the only way of explaining them was that super-intelligent aliens must have come to Budapest in the late 19th century and had babies with the locals. Von Neumann was the most alien of the lot.

But there was some accident of history that meant that European university departments at that time were disproportionately Jewish, and Belle Epoque Budapest, which was going through a less than usually antisemitic period, had a large and well-integrated Jewish population. Von Neumann himself speculated that insecurity drove this Jewish success – they recognised that Hungary’s tolerance might evaporate at any moment, and that they faced “the necessity to produce the unusual or face extinction”.

The tolerance did evaporate, in Hungary and elsewhere. Von Neumann, along with Teller, Wigner and the rest, had already left for Princeton, but Nazi persecution of the Jews in Germany devastated their universities: 15% of physicists and 19% of mathematicians were dismissed, including 20 who had won or would win Nobel prizes.

Ironically, this may have lost Germany the war: analysis suggests that the loss of Jewish scientists damaged German science for decades. Werner Heisenberg, a German quantum physicist, was branded a “white Jew” for believing in Einstein’s theories, despite being a nationalist. He later said that it was not worth Germany pursuing nuclear weapons, because they wouldn’t be ready in time to affect the war: he believed this because he thought Germany’s nuclear research was well ahead of other nations. “As it was,” says Bhattacharya. “Until 1933.”

So von Neumann, along with Wigner and others, ended up in Princeton — and then at the next-door Institute for Advanced Study, a sort of intellectual all-star team, where great brains were enticed from around the world with vast salaries, no undergrads to teach, and the promise that they could just think big thoughts. Einstein was there, along with Gödel, Robert Oppenheimer, Freeman Dyson, Ulam, and a host of others. It was an environment made for a certain kind of hard-to-pigeonhole genius, able to wander from subject to subject simply by walking around campus, surrounded by brilliant weirdos.

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Von Neumann is compelling evidence, I think, that individual genius is important and influential. Yes, he worked in a series of collaborations; yes, he built on the work of others. But he seems to have pushed on, or sometimes simply created, entire subfields of science, into areas that no one else realised could exist. The economist Oskar Morgernstern remembers him rapidly devising utility theory, immediately overturning economic orthodoxy: “But didn’t anyone see that?”, von Neumann asked.

It’s fashionable to say that intelligence isn’t real, or that we can’t define it, or that it’s a Western colonial construct. But the word points to a real thing: there is some quality which rocks don’t have, and which mice have a bit of, and which chimpanzees have more of, and humans have a lot of; and which is something like problem-solving ability or ability to achieve goals. Calling it intelligence seems as good as anything. It is this ability which has allowed humanity to shape the world, and it is this ability that some people – von Neumann among them – seem to have in unusually large measure.

Via scientific and technological progress, intelligence has made human life better. But in itself, intelligence is morally neutral: it can serve any end, good or ill, to which it is put. Von Neumann is a case in point. He developed computers partly to better predict the behaviour of explosive shockwaves and ballistic shells; he designed two different kinds of nuclear weapon, including the plutonium implosion bomb that was dropped on Nagasaki; his game-theoretic ideas led him to suggest using atom bombs in a first strike on Russia, and he was part of the inspiration for Doctor Strangelove. He believed that all this was in the interest of America, his adopted country, and no doubt of humanity; but not everyone would agree with him. First and foremost he wanted to solve puzzles.

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I put the book down wondering if it is still possible to encourage and harness genius. Perhaps it’s as simple as putting lots of clever people together and letting them think weird thoughts — and Von Neumann and his colleagues were often weird people. Or perhaps it is a true accident, and the only lesson is randomness. Perhaps the proposed new field of research into “progress studies” will yield some ideas as to how to recreate that environment in which Von Neumann and his fellow weirdos flourished.

I wondered, too, if John von Neumann was well enough to understand the P vs NP puzzle when he received that letter from Gödel. For it is a wonderful metaphor for genius. I can dimly understand, for instance, Turing’s solution to the “Halting Problem”, or Gödel’s incompleteness theorem, or Russell’s set paradox that undermined mathematics. (They’re all based on the “liar paradox” – the statement “this statement is a lie”, which is false if true or true if false.) But it often takes no great brilliance to understand an idea once it has been brought forth: checking the Sudoku solution is relatively straightforward. Finding that idea in the space of possible ideas, though — solving the great sprawling Sudokus of science and maths, as Von Neumann did time and again, that takes genius.

It’s not that these scares were completely unfounded. People have been convicted for deliberately infecting others with HIV, albeit through sex, not needles. And in 1989, a group of teenage girls were arrested for jabbing women with pins in New York “for fun”. But if there was ever a case of someone infected with HIV by a deliberate needle attack, I can’t find any record of it. The panic was essentially an urban legend, like razor blades hidden in Halloween sweets.

Now, students are reporting being jabbed with needles in nightclubs, not to spread HIV but to drug them. The Home Secretary, Priti Patel, has called for the police to look into it. It’s part of a wider concern about drink spiking, which recently inspired a national boycott of nightclubs, “Girls Night In”. But how worried should we be?

First, let’s talk about needle druggings. There are stories. In the US, for instance, eight people died at a festival, apparently crushed during a crowd surge. There were unrelated reports of a security guard and others being jabbed with a needle. But none of them have been confirmed.

And jabbing someone with a needle is not easy. One academic I spoke to said that “incapacitating someone with a needle is something the NKVD [the Russian secret police] would have to do”. It hurts, for a start; and getting it into the bloodstream via the leg or back is “really inefficient”. The dosing would be amazingly hard to get right, as would doing it without someone noticing for the several seconds it takes to press the plunger on the syringe. The idea that it’s widespread is incredibly unlikely.

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But there are claims, meanwhile, of an “epidemic” of drink spiking. There’s certainly been an increase in the number of cases reported — Harry Sumnall, a professor of substance use at Liverpool John Moores University, says that the number of police-reported incidents has gone up from about 150 in 2006 to 500-600 a year more recently. Of course, that’s not the same as the actual act becoming more common.

Academics and experts I have spoken to are all careful to say that it is important to listen when people say they’ve been drugged. But it’s also important to remember that people’s subjective experiences are not always reliable. As Paul North, a former drug treatment worker and the director of non-profit advocacy organisation Volteface, told me, there are a huge number of anecdotal reports. But it’s really hard to get any data.

So, you can record the number of people who think they’ve been spiked — about 5% of victims of rape and sexual assault think they were drugged, as Sumnall points out. But we know from every other area of psychological science that self-reported data is really unreliable. Ivan Ezquerra-Romano, co-founder and director of the social enterprise Drugs And Me, is also doing a PhD in cognitive psychology, and he says “self-report is something that we as psychologists try to run away from”.

There are a few studies that take toxicology samples from people who come to A&E reporting that they have been spiked, but not very many. Those that do exist tend to find little evidence of drugging. This one from 2007, for instance, sampled the blood and/or urine of 75 suspected spiking cases. They found unexplained drugs (that is, drugs the patient denied taking) in eight of the samples; three of them were cannabis and three were MDMA. GHB and benzodiazepines were each found in one. The report concluded that the use of sedatives to spike drinks “​​may not be as common as reported in the mainstream media”.

A more recent review of the literature, which looks at a variety of studies from around the world, found similar results. It concluded that drink-spiking “appears to be an un-common occurrence in drug facilitated sexual assault”. “In the majority of cases that get investigated, the substances are impossible to detect,” says Giulia Zampini, a senior lecturer in criminology at the University of Greenwich.

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There are possible explanations for this, of course. One is that GHB, in particular, is very quickly metabolised, especially if authorities are slow to take samples; so perhaps there’s under-reporting of spiking incidents. But equally, as an academic I spoke to pointed out, sometimes it’s easier to say – to the police, or one’s parents or employers – that you were drugged than that you took drugs recreationally.

This doesn’t mean that “drug-facilitated sexual assault” isn’t a real and widespread problem. It’s just that on the whole, the people — the, let’s face it, men — who are doing it don’t need to use fancy drugs, and don’t need to slip it into a stranger’s drink in a nightclub. The focus on one particular archetype of “spiking” — the stranger slipping a clear liquid into a woman’s drink in a club, as in the BBC drama I May Destroy You — distracts from a more complex social problem.

“The stranger-danger archetype is very rare,” says Zampini. “In every aspect of rape and sexual assault, it is much more common in an environment where there’s a relationship of some kind.”

And in most cases of drug-facilitated sexual assault, the drug is alcohol. Sometimes it’s the perpetrator buying stronger drinks than the victim expected — a double instead of a single, say. Sometimes it’s that the victim voluntarily drinks more than they are able to handle, and the perpetrator then takes advantage. “There’s a lack of recognition of how vulnerable alcohol can make you, if consumed in large quantities,” says Zampini. “And there is a dangerous binge drinking culture in universities — drinking is down compared to past generations, but still prevalent. And people can confuse the effects of alcohol with other drugs.”

But as North and Zampini say, the important thing is the intent to harm, not the particular drug used to do it. If a man buys a woman drinks with the intent of getting her too drunk to consent, or takes advantage of a woman who is already too drunk to consent, that’s just as bad as slipping illicit drugs into her drinks.

The concern is that, in a rush to be seen to be doing something, authorities and nightclubs might start cracking down on drugs — more CCTV, more searches at the door. “These tougher responses don’t work,” Zampini says. “If people want to sneak drugs into clubs they’ll do so.” What should be happening — and pleasingly is happening — is a focus on protecting people rather than cracking down on drugs. “There have been some recent good steps that have been taken in providing harm reduction,” says Zampini: things like identifying harassers on the dance floor, and throwing them out, rather than throwing out people for “taking half a pill”.

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Even if drugs were eliminated, as long as there’s a binge-drinking culture in UK universities, drug-facilitated sexual assault will remain a risk. Spiking probably isn’t a major driver of it — although Ezquerra —Romano notes that on the gay scene, where GHB and GBL are used recreationally for chemsex, it’s more likely, because the drugs are widely available. But in the university-nightclub scenario that most of the recent media attention has been on, alcohol is probably the main cause.

We can’t blame victims, but we shouldn’t be reluctant to suggest measures to protect themselves. “There’s nothing wrong with encouraging people to take steps to ensure their own safety,” Zampini says. “But the onus of responsibility shouldn’t be on women. Otherwise it’s like the old idea of women dressing provocatively and asking for it.” So the authorities need to facilitate those steps. For instance: if we’re going to say to women that they should cover their drinks, then clubs should provide lids.

And it’s worth getting better data on drink spiking, in case it’s more common than we think. That’s something Ezquerra-Romano is working on: for instance, he suggests drug vapes that record the amount you’ve taken via a smartphone app, so that if you do a blood test later you can compare the expected blood concentration for the amount you’ve taken with what is actually found. (Existing “colour dye” tests for spiking drugs are “worse than useless,” in Sumnall’s experience.)

But it’s more likely that drink spiking is rare, focused on by the media because the narrative is simple: the innocent victim and the calculating, predatory stranger. I worry (and everybody I spoke to for this piece agreed) that focusing on these sympathetic but unrepresentative cases is a bad idea. A woman who gets drunk voluntarily is just as much a victim, if a man takes advantage of her, as is someone who is drugged. It’s not that drink-spiking victims are the “real”, or the virtuous, ones.

The Nineties-era HIV scare died away. But fascinatingly, the recent surge in “jabbing” scare stories has revived it: false stories of young women infected by HIV in UK cities have been circulating alongside the drug-scare ones. Those stories we know are untrue; I suspect that drink-spiking is real, but rare. But it’s definitely true that men take advantage of drunk women. That’s just as bad, and far more common. Creating extra reasons for women to feel afraid does not seem wise.

Are things quite that bad? Well, if you read the headlines, yes: we’re on course for disaster and we don’t seem to be turning. “Business as usual” will lead to a catastrophic collapse of Himalayan glaciers; and devastating heatwaves in the southern United States, according to the New York Times. What’s more, the same newspaper said last week ahead of COP26, “If we continue with business as usual, by the end of the century, it will be too hot to go outside during heat waves in the Middle East and South Asia.”

But all these stories share one thing: they are based on the IPCC’s RCP 8.5 scenario. It’s not an exciting name, but 8.5 is often described as “business as usual”. Hence the headlines.

RCP 8.5 is not business as usual, though; it’s an unlikely worst case. This means a large fraction of the public debate on climate change mitigation is driven by an increasingly implausible scenario, which was unlikely when it was proposed, and is even less so now. The more we focus on this scenario, though, the more pessimistic — and the more hopeless — the situation will seem.

Back in 2007, after the fourth IPCC report was published, climate scientists wanted to create new emissions scenarios, imagining how they might change in the coming decades and what impact they would have on the climate. But while there was a plan to make in-depth scenarios ahead of the fifth report in 2013, scientists would need to start running models for that report as soon as 2010. They needed something that could be used in the meantime.

So four “representative concentration pathways”, RCPs, were developed: RCP 2.6, 4.5, 6.0 and 8.5[1.  The numbers refer to the amount of “radiative forcing”: that is, the change to the Earth’s energy balance, in watts per metre squared. So under RCP 6.0, each square metre of the earth’s surface would absorb six more watts of energy than it would lose to space, relative to pre-industrial times.]. They were, roughly speaking, a highly optimistic scenario regarding emissions, two middling scenarios, and a highly pessimistic one. RCP 8.5 was there as a realistic worst case. Even at the time, it was viewed as unlikely. But two unfortunate things confused that picture.

First, of the four RCPs, only 8.5 imagined a world with no climate policy. And second, its authors described it as “a high-emission business as usual scenario” – meaning that it was at the high end of emissions for business as usual, but which was taken to mean in some quarters that high emissions were business as usual.

So RCP 8.5 became synonymous with “business as usual”. There are dozens of studies published every year describing it as such: this one says that “Business-as-usual will lead to super and ultra-extreme heatwaves in the Middle East and North Africa”. It is a mainstream part of climate science.

“The problem isn’t that people study these scenarios,” says Zeke Hausfather, a climate scientist at the Breakthrough Institute. “It’s that they frame them as the most likely outcome in the absence of policy. That was probably never true, but I’m sure it’s not true now.”

Most obviously, we don’t live in a world without climate policies. The Paris protocols commit signatories to goals of keeping warming below 2°C and aiming for below 1.5°C. So “business as usual” is a global effort to reduce emissions.

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Second, and most important, the world has changed its energy mix far faster than RCP 8.5 expected. Hausfather points out that “current solar prices are below what the models think they’ll be in 2050”, and coal use has dropped in recent years. In order for emissions to reach what RCP 8.5 imagined, we’d need to increase coal use per capita by about 700% from today’s levels, spectacularly reversing the recent decline.

And this means that the really devastating levels of warming envisioned by RCP 8.5 are even less likely: we’re probably looking at a situation of 3°C warming above pre-industrial levels, compared with the 4°C, 5°C or even 6°C that RCP 8.5 predicts. As Hausfather said in a Nature piece last year, 3°C is still “a catastrophic outcome”, but it’s a heck of a lot better than it used to be.

It’s worth noting that while the emissions that get the world to RCP 8.5 are highly unlikely, the outcomes that 8.5 predicts are still plausible. One of the uncertainties in the climate is the feedbacks. For instance, as the world gets hotter, ice melts. Ice reflects sunshine, while dark earth absorbs it. So the less ice there is, the faster the world warms, reducing ice. That’s a positive feedback system: it tends to accelerate changes.

On the flip side, the more CO₂ there is in the atmosphere, the faster plants grow, and the more they suck from the atmosphere. That’s a negative feedback system, and it tends to moderate changes.

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The exact magnitude of the many possible feedback systems in the atmosphere is uncertain. As the Siberian permafrost melts, will it release gigatonnes of CO₂? It is possible that even under more optimistic emissions scenarios, the feedback systems will push more carbon into the atmosphere, so we end up with the same CO₂ concentrations as we would have done under RCP 8.5. And there’s another layer of uncertainty, which is “climate sensitivity”. It’s not precisely known how much warming a given increase in CO₂ concentrations will cause; perhaps even if the amount of carbon in the atmosphere is relatively low, the warming will be greater (although that now looks less likely).

So really bad outcomes are possible even without RCP 8.5-level emissions. We’d have to get “incredibly unlucky” with feedbacks for the world to end up warming that much, says Hausfather, but we can’t eliminate the possibility.

As a result, there’s an ongoing row among academics about whether RCP 8.5 (and its equally pessimistic successor[2.  In the latest IPCC report, the RCPs have been replaced with something else, called SSPs. Only one of the new SSPs, SSP5, is compatible with RCP 8.5. Research using both is combined into a scenario called SSP5-8.5. I’m going to refer to RCPs throughout, for simplicity.]) should be used at all. Roger Pielke Jr, a professor of public policy and environmental studies at the University of Colorado, Boulder, argues that it essentially has no place: that it’s based on an implausible scenario, even if you could end up somewhere similar because of uncertainties elsewhere. “Two wrongs don’t make a right,” he says.

But Richard Betts, a climate scientist at Exeter University and the Met Office, and one of the authors of the UK government’s climate change risk assessment, argues differently. We need to be aware of the risks of these 4°C worlds that we could – plausibly – still find ourselves in, he says. And there’s been lots of research done on worlds like that, mostly using RCP 8.5.

Gavin Schmidt, the director of NASA’s Goddard Institute, adds that using extreme scenarios allows modellers to tease out causality in a way that more narrow ones don’t. “It allows you to see non-linearity, allows you to look for thresholds,” he says. “You get a stronger signal to noise ratio.” The actual emission scenarios are less likely, he agrees, but studying them helps us understand the climate system better.

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The trouble is that if a journalist sees two graphs in a study, one of a plausible RCP 4.5 world and one of a much more dramatic RCP 8.5 world, they’ll probably want to run the RCP 8.5 graph, “because it’s more impressive,” says Hausfather. “And they’ll say scientists predict this much sea level rise, or this many heat deaths. And it’s not accurate because you’d need to add ‘but only if we burn all our coal or get very unlucky’.”

There’s also a risk that RCP 8.5 will crowd out research into other areas. For instance, Our World in Data’s recent efforts to understand human impacts on biodiversity have been hampered because almost all the research into coral reef collapse has been carried out using RCP 8.5. Predictably enough, all the coral dies in that scenario. But what would coral reefs look like under 2°C of warming, or 3°C? We don’t really know.

(People have got in touch to suggest that recent studies have looked into the other pathways, and they do seem to show severe impacts on coral even at lower concentrations.)

The RCP 8.5 debate is heated. Pielke Jr and Schmidt in particular have a longstanding mutual animosity, and Schmidt feels Pielke Jr has “spent 20 years trying to elbow out scientists from the centre ground” on climate change. So it’s hard for a journalist to get involved without taking sides.

But it’s important, because the more we associate the worst-case scenario with business as usual, the more pessimistic the public debate will be. “It might demotivate people,” says Hausfather. “It’s much easier to see the Paris goals [of no more than 1.5°C warming] as achievable if you know we’re on course for a 3°C world, rather than a 5°C one.”

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I disagree with Pielke Jr: RCP 8.5 has its place, because as Betts says, there’s a whole range of research into unlikely but plausible scenarios using it, and if you’re doing risk assessments, you need to look at outcomes that probably won’t happen but which would be disastrous if they did, so you can try to avoid them. At the moment the papers looking at those scenarios all use 8.5.

But on the other hand, if the IPCC puts out RCPs without explicitly saying which are the most likely, then policymakers and journalists will take whichever scenario most suits their needs, whether that’s pretending there’s no problem, or magnifying the problem for the sake of a headline. This isn’t the fault of climate modellers. But no one involved in the IPCC is explicitly saying “RCP 8.5 is pretty unlikely,” and that fateful phrase “business as usual” is still attached to it.

Hausfather has a solution for this: attach explicit percentage likelihoods to the different scenarios – say that RCP 4.5 is 45% probable, or RCP 8.5 is 5% probable. They’d be necessarily subjective but at least it would show that no one thinks they’re all equally likely, or that RCP 8.5 is the course we’re already on. Betts agrees.

There’s a risk that climate sceptics will leap on ideas like this, and say that climate scientists have systematically overstated the risk of climate change; Betts says that this already happens. But climate change is an unhedgeable risk, and we need to pay attention to even unlikely outcomes if they’re very bad, in the same way that you wouldn’t play Russian roulette even if it’s only a 17% chance of death. Besides, even the less dramatic 3°C worlds involve dangerous levels of sea level rise, heat waves, and millions of unnecessary deaths a year. You’d think that’d be worrying enough.

Every week, people see headlines, based on RCP 8.5 scenarios, that say things like three billion people could face “near-unliveable conditions” by 2070. Understandably, people are rattled; and some are even saying they’re not having children. People are more scared than they need to be.

Climate change is extremely bad. But we’re not in the “last chance saloon”, if that means that we face some inevitable catastrophe, or 5-1 down and facing ruinous defeat; and the politicians at COP26 are not guilty of facilitating a worse genocide than Hitler’s. Archbishop Welby apologised for that. But it’s an understandable mistake if he’s always being told that “business as usual” means a march to doom.

But after an autumn of absolutely pathetic numbers — for a while we were barely jabbing 40,000 a day, first and second doses combined, down from ten times that number and more in the spring and early summer — the programme does appear to have picked up a little. There were hundreds of thousands of booster jabs alone given over the weekend.

The Government has pinned its entire winter strategy on those boosters. Sajid Javid, the health secretary, has said Plan B, will not be introduced because of them. Plan B is a milquetoasty set of minor interventions, including greater use of vaccine passports, and legally requiring masks in some places. They may ask for people to work from home if things get worse. It’s not exactly Protect and Survive.

And we’re not going there. Yet.

But there is a problem, which is that Covid-19 in the UK has become really hard to predict. Last year, whatever you looked at — cases, hospitalisations, deaths — the numbers followed a simple pattern. They started going up slowly, then they went up quicker, then quicker, then quicker. Then the Government imposed a lockdown, and, a little while and a few thousand more dead people later, the numbers crashed back down again.

But this year, the pandemic is no longer doing nice straightforward exponential curves. If you look at hospitalisations over the last six months or so, you’ll see that they started a familiar-looking rise in about June, and they clambered up to about 1,000 a day in late July and then – they just stopped, and went back down a bit. Then they rose again, and back down. Now they’ve started going up again, but … will that carry on? Will the curve fizzle out again? Unlike last year, it’s not easy to tell what will happen next.

Adam Kucharski, a mathematical epidemiologist, explained why winter 2021/22 is trickier to model than 20/21. First, he says, in any epidemic, “When R is near 1, it’s a headache.” R is the number of people that each infected person goes on to infect, on average. So if your initial 100 cases lead to 200 subsidiary cases, then your R is 2. If R is above 1, your epidemic is growing exponentially; if it’s below 1, it’s dying away.

When R is well above 1, it’s fairly easy to predict what will happen: cases will go up, faster and faster, until it runs out of susceptible people or until policies and behaviours change so that R goes below 1 and the epidemic declines again. That’s how it was last year.

But now, thanks to vaccines and natural immunity, the number of susceptible people around is lower — we’re close to herd immunity — so even with essentially no restrictions, R is close to 1. And when it’s close to 1, smaller things can affect the course of the epidemic much more. “It bounces around,” says Kucharski. “Schools reopening, or closing for half term. The vaccine rollout and boosters. If the epidemic is increasing or decreasing, some small change can knock it the other way.”

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One way to think of it is that if your R is 2.5, and, for instance, schools close for half term, then your R might go down to 2.3. It’s the difference between an epidemic that’s spreading quickly, and one that’s spreading not quite so quickly. But if R is 1.1 and you knock it down to 0.9, then it’s the difference between a pandemic that’s growing and one that’s shrinking.

It also means that individual behaviour becomes important. Around our way, it recently became very obvious that lots of schoolchildren (and their parents) were getting the disease. So when ours got the sniffles we were quicker to take them out of school, and more wary of going to shops. Presumably millions of people have behaved in similar ways. It wouldn’t have made much difference last year — the disease was spreading so rapidly — but it could be enough now to change things from an up-arrow to a down-arrow.

There’s a more interesting dynamic at play as well, says Kucharski. Imagine the first two waves of the pandemic as a forest fire in a virgin forest. Once a tree starts burning, it easily spreads to the trees around it, and, left to its own devices, the fire will carry on until the forest has burnt away. An immunologically naive population is the same. “When you have a fully susceptible population,” says Kucharski, “the simple exponential curve models will give you something sensible.”

But we no longer live in a virgin forest. Vaccinations and acquired immunity means that the large bulk of the population is less susceptible to infection. In our analogy, you could imagine that it’s a far sparser forest, with occasional clumps here and there representing groups with lower vaccination uptake. Now, if a tree catches fire, it might not spread to any others. Or it might be in a clump, and spread around that clump quickly, and then burn out. Or a spark might make it from the clump to a nearby clump. It’s much more dependent on luck and randomness.

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“You have a much patchier network for the virus to spread in,” says Kucharski. “A lot more dead ends, and surges in younger groups and places with lower vaccination cover; you get pockets of susceptibility, and you get large outbreaks, but it has to work its way from one pocket to another. When you’ve got lots of these happening in parallel, you have a much bumpier dynamic, more conducive to a bobbling-along plateau than one that has clear growth or decline.”

All of which makes the epidemic much harder to predict. But there’s another layer of difficulty on top of that, which is that you’re not just predicting an epidemic: you’re predicting human behaviour. More specifically, you’re predicting the behaviour of the current government and parliament. There is not an algorithm which says “At 1,500 hospitalisations a day, we re-enter lockdown” or anything.

Other countries do have something like that — in Taiwan, for instance, there are four levels of restrictions, which are imposed on a specified set of conditions. Level 1 is imposed if there are cases imported from overseas that result in “isolated community transmission”; Level 2 is imposed if there are “domestically transmitted cases from unknown sources”, etc. If you could predict the behaviour of the epidemic in Taiwan, you’d have a pretty good basis for predicting the policy response to it.

But not in the UK. Plan B, or any further restrictions, will be imposed if the Government decides to impose them. I think that’s a bad system, and the Taiwanese method of removing the decision as far as possible from political pressure would be better. But that’s the system we have. So as well as predicting what the virus will do, we have to predict how 361 Tory MPs will respond to the virus, and how Boris Johnson and Sajid Javid will respond to them. So predicting whether we’ll end up in a lockdown is hard.

But perhaps there’s a more interesting question, which is: should we act as though we might have to, and start doing something about it now? Should we impose Plan B?

For all the current doom and gloom, we are in a better place than even the most optimistic of the government’s SPI-M-O models projections from early September. And those models now tend to forecast a decline in cases from later this month, even if we don’t impose Plan B. We’re not facing an immediate crisis.

Oliver Johnson, a mathematician at Bristol, says an autumn decline is plausible: “The key thing is it has been so crazy in schools, with 7% or 8% of schoolkids infected at any one time,” he says. “You can’t have that forever: you’ll run out of people. So it’s a case of working out when you’ll run out of people.” The little clumps of forest will burn down eventually.

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The pandemic, though, has been consistently hard to predict and the great failings of the British Covid response have been trying to be too clever, doing things too late, and not thinking about risk-benefit calculations or worst-case scenarios.

This feels very much like one of those situations. It’s true that, probably, if we leave it and do nothing, things won’t get that much worse, and may soon get better. But there’s a non-trivial chance that they will get worse. Given that the costs of imposing Plan B restrictions are minor, why not take simple, low-cost steps now to avoid the unlikely, but plausible and very bad, future shit show.

We have a suite of tools available to us – not just Plan B, but also things like increased rapid testing – that can avoid future disruption. But, also, if we impose them now, we can undo them later easily if they’re unnecessary, or tweak them if they’re not quite right.

For instance, there are concerns that vaccine passports will damage the entertainment industry and have only a small impact on the virus. If that’s true, we can reduce their use once that’s clear.

And we should be able to avoid stringent restrictions being imposed again, and another catastrophic Christmas lockdown. “Given the tools we now have available,” says Kucharski, “it would be completely absurd if we got to that point.”

I agree. We absolutely should not need to consider a Plan C, one in which we shut stuff down, stop people seeing their loved ones, close the pubs, ruin Christmas. And we can make it all but certain that we don’t have to do that if we take small, sensible precautions now. Hopefully enough elderly people will stand in cold car parks to get their boosters; but let’s not rely on it entirely.

Prevent is a much-mocked government counter-terrorism strategy, which looks out for warning signs that someone is at risk of being radicalised into some form of violent extremism. About 6,000 people are referred to it each year. 

Every high-profile murder or act of terrorism appears to be followed by a story like this. The Manchester Arena suicide bomber was known to the security services. Lee Rigby’s killers were known to the security services. At least one of the London Bridge attackers was known to the security services. It’s not just in cases of terrorism: the murderer of Alice Gross had a criminal record for sexual assault. Sarah Everard’s killer had a history of indecent exposure.

There is a further sense of inevitability over the fact that, three days after Sir David’s murder, Priti Patel, the home secretary, declared that Prevent is “under review” to ensure that it is “fit for purpose”. This is, in fact, an ongoing review that was launched in 2019 and due to report in December, rather than a response to the atrocity. Yet the announcement was clearly intended to suggest that the murder represented a failure on the part of the surveillance and deradicalisation system.

But is it? The sense of inevitability is real: these stories crop up a lot. But the truth is they will continue to keep cropping up, almost every time there is a terror attack, and almost no matter what we do with Prevent or whatever its successor is. Prevent may be failing — though I’ll happily defer judgment until the publication of the review — but even if it were successful, when terrorist attacks happen, it is likely that the perpetrators would be on its books, or known to the security services.

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And more than that: as callous as it sounds, that may be for the best. To give a sense of why that is, it’s helpful to look at another system for spotting future risks: cancer screening.

You screen for prostate cancer with a blood test, which looks for levels of something called “prostate-specific antigen”, or PSA. If you’re between 50 and 69 years old, the “normal” level of PSA is given as below three nanograms per millilitre (ng/ml). If your test comes back higher than that, you are flagged as at risk of having prostate cancer.

But according to the NHS, about 15% of men who do have prostate cancer actually have “normal” levels of PSA: that is, below 3ng/ml. If you run a screening test like this, you’ll falsely reassure about one cancer sufferer in six that they are cancer-free.

There’s a straightforward solution to that, of course. Instead of putting your cut-off at 3ng/ml, you could make it 2.5ng/ml. Then you’d miss fewer cancers.

You can probably guess the outcome, though. If you move the cut-off lower, you’ll miss fewer real cancers, but you’ll scare more men unnecessarily. If you move the cut-off higher, you’ll get fewer false positives — you won’t tell as many men that they have cancer when they don’t — but you’ll get more false negatives: you’ll miss more real cancers.

You might think that it’s a pretty straightforward decision. Telling people that they have cancer when they don’t is inconvenient and alarming; telling people that they don’t have cancer when they do might kill them. So you err on the side of caution.

But that’s not how it works. False positives on cancer screenings can literally kill. People can end up having unnecessary surgeries, X-rays, chemo or radiotherapy. Complications are sufficiently common that the NHS says that the benefits of prostate cancer screening do not outweigh the risks.

The cost of getting it wrong is real with terrorism, too. The process is somewhat different, though. When it comes to the risk of someone being radicalised, unlike a blood test, there isn’t a number that you can read off. That doesn’t mean there can’t be one: it could be that the people who are passed on to Prevent are given a quasi-objective score. We do exactly that for, say, suicidality risk or autism or happiness, or any one of a thousand psychological functions. You tick boxes on a questionnaire about someone’s isolation, their anger, their ideologies, and if the score on the questionnaire adds up to more than 40 or whatever you declare them a terrorism risk. That is precisely what goes on, in a less obvious and open way, with things like AI parole decisions. 

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But as it happens, there isn’t an explicit number. The Prevent guidance says: “There is no fixed profile of a terrorist, so there is no defined threshold to determine whether an individual is at risk of being drawn into terrorism.” So there’s no nice straightforward “Terrorism risk: 13.6” readout.

Nonetheless, the same process is going on. A person comes into contact with the counterterrorism services. Of the 6,000 or so who are referred to Prevent each year, about 500 or so are deemed “vulnerable” to radicalisation and are passed on to a subgroup called “Channel”. 

Since referral to Channel is not based on an objective score, it’s based on a subjective feeling (or “expert judgment”): people will be flagged up if the panel judging them feels sufficiently strongly that they’re a risk. They might not explicitly say “Terrorism risk: 13.6”, but still, there is a threshold of risk, over which someone is considered a threat.

And just like the PSA levels in the blood, that assessment will be imperfect. If you read some young man saying something disturbing online, is it harmless anger and braggadocio, or are they a terrorist? You can raise your implicit threshold and avoid harassing innocent people, at the cost of an increased risk of missing a genuine terrorist, or you can lower it and correctly identify more terrorists, but at the cost of labelling a lot of harmless people as potential terrorists.

It might seem, like the cancer test in reverse, that there’s an asymmetry here: a false positive annoys people; a false negative kills people. But also like the cancer test, it’s not as simple as that. The false positives will be a lot more common than the false negatives — simply put, there are more mouthy non-terrorists than actual terrorists in the world. As I said before, there were 6,000 people referred to Prevent every year, and 500 or so were judged to be of sufficiently high risk to be passed on to Channel. But there have been only four actual terror attacks in the last two years. 

If you lower your threshold, raise the alarm on more borderline cases, then you will waste more police time, put more innocent people under needless scrutiny and stigmatise more communities. (It seems inevitable that a lowered threshold will mean more young Muslim men, in particular, being picked up by the security services.) That may be a price worth paying — but, let’s be clear, it will be a price that you pay. If you take in everybody suspicious for questioning — every misogynist loser on incel subreddits, every angry racist or radical Islamist on dark-web chatrooms — you may prevent one or two more attacks, but you will undoubtedly fill up your jails and enrage the populace.

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This doesn’t mean there’s nothing you can do. Moving your threshold is zero-sum. But you can change your test: if instead of testing for PSA you looked for some other marker, you might be able to tell whether someone had cancer with more accuracy. In the case of counter-terrorism, you could do things like increasing police funding for surveillance, rather than simply being more strict about your criteria — although of course that would mean less money elsewhere.

An alternative suggestion might be to abandon “expert judgment” and introduce something like I talked about above, an explicit algorithm: human judgment is famously terrible at predicting complex things like the likelihood of a criminal to reoffend, and algorithms consistently outperform us, as the psychologist Paul Meehl demonstrated way back in 1954. They beat humans at predicting the price of wine, how long a cancer patient will live, who will win a football match, how likely a business is to succeed and dozens of other subjects. It is likely that some fairly simple algorithm could do significantly better than the best experts at predicting who is a terror risk, as well.

But it could only ever be a partial improvement. Humans are stubbornly hard to predict. Part of the reason why algorithms can outperform human judgment in those fields is because human judgment is consistently terrible: we are very often wrong about who will reoffend, who will live and die, who will win a football match. It is not that algorithmic prediction is great; the future is still hard to know. However good we make our systems for detecting terrorists, they will never be very good. So terrible things will always happen, and when they do, we will assume our systems are too lax and need to be tightened.

It is tempting to think like that in the wake of an atrocity such as David Amess’s murder: to think that we ought to lower our thresholds of what counts as a risk. Perhaps it’s even true. But just as it is an unavoidable fact of reality that reducing false alarms means missing more real ones, a world in which Sir David’s murderer was caught ahead of time could be grimly authoritarian.

It seemed reassuring. I know this because I messaged something like “It seems like they might know what they’re doing!”  in a science-nerdy chat group that I’m part of. But my optimism was not echoed. “It seems like they’ve just killed all our grannies,” responded someone.

I recall this to make two points. Firstly, I can’t claim any sort of foresight. I didn’t see then, though perhaps I should have done, that the early response of the British government to Covid was disastrous and wrongheaded. Instead, I was falsely reassured by the confidence of the scientific advisers, Whitty and Vallance. 

But secondly, other people did see it. The failings were predicted. And not just by my friend: a large number of smart, numerate generalists outperformed public health experts repeatedly in predicting the course of the pandemic in those early months.

On Monday night, two House of Commons select committees released the findings of their report into England’s Covid response. The headlines yesterday were stark: the “worst public health failure ever”, “big mistakes”, “damning”. 

The individual mistakes — failure to protect care homes; failure to move quickly enough on lockdowns; failure to build testing infrastructure and more — are certainly worthy of criticism. But having read the report, I want to talk about what strikes me as the overarching theme: being too bloody clever by half.

Let’s return to early March 2020, when I was falsely reassured by the Whitty/Vallance/Johnson press conference. The “flatten the curve” strategy that the three men introduced involved slowly introducing public health measures one by one. The idea was to carefully control the R value – the number of people that each infected person would, on average, give the disease to. The idea was to keep it at just above one, so that the disease would slowly spread through the population rather than either burning through it or dwindling down.

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In paragraph 87 of the report, the committee writes: “It is striking, looking back, that it was accepted that the level of Covid-19 infection in the UK could be controlled by turning on particular non-pharmaceutical interventions at particular times.” 

“Striking” is certainly one word for it. This was a novel disease. We knew almost nothing about its behaviour. On March 6 2020, I went to a gig at Alexandra Palace. Looking back now, it should not have been allowed to go ahead — but because the consensus at the time was that the disease was spread by fomites, by touching surfaces, the only advice was to keep washing our hands. In fact, it is spread largely through the air. 

So we had a completely wrong idea how the virus behaved, but we nevertheless had a confident plan to control it very precisely by now banning large gatherings, then closing schools, then encouraging working from home. We were trying to thread a needle blindfolded.

This sort of overconfidence in highly uncertain science crops up over and over again. As well as flattening the curve, the phased introduction of distancing measures had another purpose: it was not believed that the public would accept long-term restrictions on their freedom. “There is a risk,” Whitty told the public in one of the early press conferences, “that if we go too early, people will understandably get fatigued and it will be difficult to sustain this over time.” 

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This was also the official Sage position: “Sage agreed that a balance needs to be struck between interventions that theoretically have significant impacts and interventions which the public can feasibly and safely adopt in sufficient numbers over long periods.”

It’s not clear what this claim was based on. Other behavioural scientists questioned it, with 200 of them signing an open letter to the Government asking for the evidence. But what evidence were they hoping to receive? What comparable situation have we ever faced? A few small-scale experiments about people obeying instructions under laboratory conditions probably can’t tell us anything at all about what people would be willing to do in order to save hundreds of thousands of their fellow citizens, very possibly including their own elderly relatives. Nonetheless, we based highly counterintuitive policy decisions — not imposing strict lockdowns even as the disease was spreading and killing, despite seeing it being successful elsewhere — on this apparently unevidenced belief.

And let’s be clear: the coming hundreds of thousands of deaths were very much known about. The report rightly says that we over-relied on a plan for pandemic flu. That was a huge mistake, and it led us to strange things like moving to “delay” the virus rather than “contain” it, and stopping community testing. 

That meant that we had no idea how widespread the virus was. “The UK was reduced to understanding the spread of Covid-19 by waiting for people to be so sick that they needed to be admitted to hospital,” says the report. “For a country with a world-class expertise in data analysis, to face the biggest health crisis in a hundred years with virtually no data to analyse was an almost unimaginable setback.”

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So, yes, sticking to the flu plan was a disaster. But if you read the flu plan, it wouldn’t have worked any better for flu. The disaster that arrived was completely foreseen, yet it confidently said we couldn’t do anything about it, so we shouldn’t try. It said that planners should “aim to cope with a population mortality rate of up to 210,000–315,000 additional deaths” within 15 weeks of a virus arriving, and should “plan for a situation in which up to 2.5% of those with symptoms die”. 

The lowest number there, 210,000 over 15 weeks, works out at 2,000 deaths a day. At the peak of the second wave, the seven-day rolling average of deaths briefly – for nine days, in fact — broke 1,200 a day. 

That period was horrifying, but the pandemic flu plan was to “cope” with at least 2,000 deaths a day for 15 weeks. That appears to have been based on the idea that we, the British public, were so profoundly wedded to liberty that we would rather let the equivalent of the entire population of Norwich die in three months than have the nightclubs closed. 

More than that, in fact, it was important to keep them open so that people didn’t get depressed: “Large public gatherings or crowded events where people may be in close proximity are an important indicator of ‘normality’ and may help maintain public morale during a pandemic.” The possibility that people might not want to attend large public gatherings if it might kill them or their family, or might value the lives of their elderly relatives highly enough to stay home for a few weeks rather than watch them die in their thousands, was not considered..

Over and over again, the British response was one of unwarranted confidence in highly uncertain outcomes. We were, of course, dealing with a largely unprecedented situation. But that meant that the evidence was necessarily weak. 

So statements such as “There is very limited evidence that restrictions on mass gatherings will have any significant effect on influenza virus transmission” (from the pandemic flu plan) were on the one hand true, but also basically meaningless. There’s very limited evidence either way. 

But the common sense attitude would be that being in a large group of people probably helps the virus to spread. And given that the alternative, which the plan explicitly endorses, is at least 210,000 people dying in 15 weeks, it seems incredible that the approach was “well, there’s no evidence that it works, so we might as well not try”.

There are other areas where this overconfident, clever-clever approach backfired. For instance, Matt Hancock told the committee that in the early months, they were advised that “testing people asymptomatically might lead to false negatives” so you shouldn’t do it. Of course, it would also lead to lots of true positives. But the fear of a hypothetical second-order effect — like Jenny Harries’s claim last March that wearing masks could increase the risk of infection, because it would make people more confident — overshadowed the very obvious and real risk of disease. It’s like the plan was based around not wanting to be obvious, but always being clever and counterintuitive.

And the reliance on the pandemic flu “flatten the curve” plan meant that there was no modelling of the impacts of a lockdown. Dominic Cummings told the committee: “You are going to have to lock down, but there is no lockdown plan. It doesn’t exist. Sage haven’t modelled it.”

Often, simply plotting an exponential curve on a graph might have done better than the more complex forecasts the Government used. Vallance told a press conference that we were “four weeks” behind Italy, when simply looking at the number of deaths showed that we were 14 days behind. And when other countries locked down, with some success, we didn’t learn from their example, because we had a plan, and we were confident in it.

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And, of course, border closures. Naively, you’d think that stopping people coming into the country would reduce the likelihood of the disease coming here. But overconfident modelling told scientific advisers that it wouldn’t, so they trusted that, rather than thinking “OK, but if the model is wrong then we’ve completely messed up.”

This isn’t to place all of the blame on Whitty, Vallance and Sage — or to exonerate the politicians. There was plenty of cowardice and dithering on the part of the Government, not least (as the report shows) when they ignored scientific advice to impose a short “circuit breaker” lockdown in autumn 2020, presumably because they feared being unpopular. 

But the valuable lesson, I think, is not “we should have done X” or “we should have done Y” specifically, but that we should be less confident in our ability to predict highly complex situations. And more than that: we should look at the possible results of being wrong in our attempts to predict those highly complex outcomes. If you’re 60% sure that lockdowns won’t work, then that implies there’s a 40% chance they will, and that implies that you think there’s a 40% chance of saving hundreds of thousands of lives. Instead, it seems like the whole way through, the thinking was “it probably won’t work so let’s not try”.

I didn’t foresee any of this. I should have: it was all there in the pandemic flu plan. Others, smarter and more conscientious than me, did, or at least foresaw that disaster was a realistic probability. But the takeaway should be not “in the next pandemic, we need to protect care homes” — although we should — but “in the next pandemic, we should stop trying to be so damn clever”.

What’s even more appalling is that Couzens had apparently committed indecent exposure twice: once in 2015 and once three days before Everard’s murder.

Cressida Dick, the Metropolitan Police commissioner, said in an unfortunate phrase a few months ago that there is the occasional “bad ‘un” in the service. She did go on to say that they are “intolerant” of violence and that they “​​work to identify and tackle and prevent any such behaviours”. But the impression was given that the occasional bad ‘un is an inevitable part of the institution.

It appears to be rather more than one, for the record. Apparently, 27 serving Metropolitan Police officers have been convicted of sex crimes since 2016. At least one is still on the force as an unpaid special constable, having joined the Met after his conviction for indecent exposure. (A simple criminal conviction does not automatically bar you from joining the police.) Another Met officer, who worked in the same unit as Couzens, is facing rape charges this week.

It’s no surprise, then, that the Metropolitan Police has been accused of institutional misogyny — and of needing drastic reform. In the wake of an awful story, there is an understandable, human need to want to make sure it never happens again. Specifically, we feel the police should, of all professions, be the one without a single “bad ‘un”.

But it’s also worth remembering that, in the coming years or decades, no matter how well we vet police officers, you’ll read some horrible story like this again. Hopefully we can reduce the likelihood; perhaps even reduce it a lot. But the numbers involved are just too large for it to be realistic that we can bring it to zero. So we need to have some idea – with this, and with every movement campaigning for some social good, from public health to racial justice – of what success looks like. And if we want to aim for zero, then we have to be honest about what the costs of that aim are.

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Here’s why I think it will be difficult to reach zero: the numbers. About 120,000 sexual offences were recorded in England and Wales in 2017, of which 40,000 were rapes. That’s a huge undercount of the actual figure committed (the Crime Survey of England and Wales estimates the true figure at four million), but let’s use it.

Presumably some of those 120,000 were committed by the same people — there probably weren’t 120,000 recorded perpetrators — but even if we half that number, we still end up at about 0.1% of the entire population of England and Wales recorded as committing a sex crime each year.

Let’s take that 0.1%, and pretend that the whole population is equally likely to commit sex crimes. There are about 160,000 police officers in the UK. That means that if police officers commit sex crimes at the same rate as the rest of the population, you would expect about 160 sex offences committed by police officers every year.

I sincerely hope that police officers don’t commit sex crimes at the same rate as the rest of the population. But even if they only do it 1% as often, you would still expect one or two every year; easily enough to fill newspapers with appalling stories. At those rates, you’d expect even rarer crimes such as rape and murder once every few years.

So let’s return to the question: what does success look like?

This is a difficult conversation for any reforming movement.Because there are costs that come with demanding too perfect a victory.

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Take a topical example: Zero Covid. You can say what you like about the movement’s aims, but at least it was explicit about them: it called for the “elimination of the virus from the UK”. If there’s still a single case of Covid in your country, you have not achieved Zero Covid, and you keep trying until you have.

New Zealand was always the Zero Covid paragon, along with Australia and Hong Kong and a few other places. As recently as August, a single Auckland case that couldn’t be traced to a point of origin led to a several-week lockdown. But this week, the Wellington government formally abandoned its policy of eliminating Covid, and moved to a plan to live with the virus (albeit, no doubt, at a much lower level of the virus than we have to live with). The costs of maintaining Zero Covid in the face of the Delta variant — and the reduced threat of the virus itself, given the vaccines — led them to decide that going for absolute zero wasn’t the best policy anymore.

In the UK, we have decided that a certain mortality rate is acceptable. Professor Jeremy Farrar, the director of the Wellcome Trust, said recently that “living with the virus” will probably mean about 100 deaths a day, or more than 30,000 a year. It’s a grim thought, but we have to be clear-eyed and say that the elimination of risk is probably impossible, so we have to tolerate some level of it.

But when it comes to violence against women and girls, what is an acceptable number of murders every year? It’s an appalling question, but it’s one we have to ask, just as we had to with Covid. And it’s worth stopping at this point and asking whether you know how many women are murdered each year right now. Give a figure, in numbers per 100,000 per year. And then say how many you think is acceptable, again in numbers per 100,000 per year: 10 per 100,000, one in 100,000, 0.1 per 100,000?

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Here’s the real figure: according to the ONS, there has been an average of about 200 women and girls murdered each year in England and Wales for the last 10 years. Out of a population of about 31 million, that is a little under 0.7 women per 100,000 per year.

To be clear: I’m not saying that’s acceptable. But if you’d just said that you thought about one woman in every 100,000 was killed every year, and you wouldn’t rest until the murder rate was below one in 100,000, then we’re already below that.

If you said one in a million, then we’ve still got some way to go. But soon you’re going to start hitting up against some really hard limits. According to the World Bank, only two countries in the world recorded 0.1 murders per 100,000 female population (that is, one in a million) in their most recent data, and those two countries were Oman and the Palestinian territories, which makes me think that the data might not be entirely reliable.

Other countries manage 0.3 per 100,000: Singapore, Japan, Macao, Norway, Qatar, and Saudi Arabia. But most of the countries we tend to think of as our peers — France, Germany, Australia, Sweden, Denmark, Canada — have roughly similar numbers. Getting the numbers much below that seems to be very difficult.

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You could argue that it does no harm to aim for zero, even if you know you won’t get there. But it’s not as simple as that. Reducing some societal harm costs money and time, and at some point, you’re going to hit diminishing returns. You’ll have to spend ever more resources for smaller and smaller improvements.

Maybe you’d say that you can’t put a price on a human life, and that’s understandable. But the reality in which we all live puts a price on human lives for you, via opportunity costs. If you spend the money on street lighting, you can’t spend it on dialysis machines, or early years education, or renewable energy subsidies. At some stage, because of diminishing returns, you’ll reach a point where spending more money on reducing murder rates means that more people die (or more people are harmed) elsewhere.

Whatever social problem you’re talking about fixing — obesity, police brutality, inactivity, racist abuse — we need to be explicit about whether we’re aiming for zero, and facing up to all the costs that will come with, or admit that you’re really aiming for some more achievable (but more painful to accept) figure.

When it comes to police reforms, there are all sorts of trade-offs to consider. One obvious reform, for instance, might be to impose stricter rules on recruitment — such as that no one with any criminal record at all can be a police officer. But I imagine that would hinder the efforts to increase the number of non-white police recruits, since young men of colour are the most likely to have criminal records. How much it would hinder them I don’t know, and perhaps the trade-off would be worth it, but you need to start by acknowledging the trade-off.

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I’m really, really not saying there’s nothing that can be done. Couzens turned up to a police work party with a sex worker, and his colleagues “jokingly” called him “the Rapist”. There is something wrong with a culture that allows that, and there are clear failures on the part of the police in investigating his earlier offences.

Most importantly, we need better data on how many police officers do commit crimes, so that we can get a sense of whether they’re more or less than we would expect. Profoundly affecting though they are, policymakers can’t make these decisions on the back of high-profile anecdotes and shocking individual stories.

Any group made up of thousands of people, such as the police force, will include some number of dreadful individuals, however hard you try to keep them out. And every so often, a dreadful person will do something dreadful.

There’s been a lot of talk of “panic buying”, but that’s not what it is. It’s straightforward rational behaviour. If you know that you need petrol, and you know that petrol stations are likely to run out of petrol, then it is entirely rational to get to the petrol station as quickly as possible and buy as much as you can fit in your tank.

The trouble is that it’s also rational for everyone else to do the same thing. So you end up with everyone blaming everyone else for being selfish, while insisting that they have excellent reasons for needing the petrol right now. You’re not stuck in traffic, you are traffic. It’s the exact situation we had with toilet rolls and Calpol in March 2020.

Over the long run, the market normally solves coordination problems like this, reasonably effectively. If lots of people want some resource, then the people selling that resource realise they can make more money if they raise the price. At the higher price, fewer people are willing to buy it, but the seller makes more money per unit sold. And, in theory, they can keep raising the price until the lost sales start to outweigh the gain per unit.

This is completely normal. Try going to Center Parcs in term-time, and you’ll find that you only need to sell one kidney for four nights. Try going in half term, and you’ll be harvesting organs from your entire street. More people want to go on the rubber-ring flumes during school holidays, so the company can charge more money for it. This is how the economy works, and normally we’re broadly OK with it apart from the occasional communist revolution.

But when companies try to raise their prices during an emergency, we call it “price gouging” and we’re really against it.

In some places, in fact, including many states in the US, it’s literally illegal. In Mississippi, for instance, there is a provision in the law that when an emergency is declared, you’re not allowed to put prices up: “the value received for all goods and services sold within the designated emergency impact area shall not exceed the prices ordinarily charged”.

This leads to some strange situations. In 2005, in the aftermath of Hurricane Katrina, a Kentucky man named Jim Shepperson bought 19 generators, and drove them 600 miles to a part of Mississippi that had suffered power outages. He rightly thought that people would be eager to buy them at twice the price he had paid for them. Instead, though, the police confiscated his generators, and threw him in jail for four days. So no one got the generators and he didn’t get the money. It’s a nice example of the Copenhagen Interpretation of Ethics: as soon as you do anything to help solve a problem, you can be blamed for not doing more.

In the UK, we don’t have price-gouging laws. But there is a strong resistance to the idea. Last year, when masks, hand sanitiser, toilet paper, paracetamol and so on were all in short supply, various retail groups put out a statement condemning price gouging: “Those who inflate prices to profit off the backs of their customers are adding to their distress at a time of particular vulnerability.”

The funny thing, though, is that most economists would tell you that price gouging is — on balance — a good thing. When asked whether “surge pricing”, increasing prices during periods of increased demand — as Uber does — increases the supply of those goods, most economists said yes. When asked whether Connecticut should ban price gouging in natural disasters, most economists said no.

That might sound strange. Jacking up prices in an emergency sounds immoral — you’re taking advantage of people’s desperation. Poor people won’t be able to afford the goods. Most people have a strong intuition that you shouldn’t be allowed to push prices up.

But look at the effects. First, if costs are artificially low — if a product is available at a cost much lower than customers are willing to pay — then it becomes rational for customers to stockpile those products. If I know that people are going to buy all the toilet paper at the store, and the toilet paper is still 50p a roll, then I can easily buy 100 rolls for £50 and stick them in the back of my people-carrier. If, however, the cost of a toilet roll had jumped to £5 a roll, I’d be much warier, and so there would probably still be toilet paper for the next person to buy. Instead, I’ve bought it all. If it was appropriately priced, it wouldn’t be so likely to run out.

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Second, appropriate pricing tends to put goods where they’re most needed. People who really need that product — in the case of petrol, people who definitely need to get to work, or to visit a distant relative, or whatever — will be willing to pay more than people who are simply worried that they might have to drive next week some time. When pricing is artificially fixed, goods will tend to end up with whoever happens to get there first, rather than the people for whom they’ll do the most good.

Third, retailers respond to incentives too. If they know they can make a decent profit on some good, then they will bring it where it’s needed. John Shepperson, the guy with the generators, wouldn’t have bothered bringing those generators if he could only get what he paid for them. (Maybe he should have driven 1,200 miles to sell them at cost, but since no one else brought any generators at all, it still seems better that he brought them.) They might also stock up on goods when they’re cheap, against the possibility that there will be some time in the future when they can sell them at a higher price.

And some emergencies are foreseeable, to an extent. As Amihai Glazer, an economist at the University of California, Irvine, says, if a retailer knows a week in advance that there’s a snowstorm coming, they might order loads of snow shovels, because they know they’ll be able to sell them at a higher price. But if they expect price controls, they will have less reason to do so. So fewer people end up with snow shovels than would otherwise get them, and you get queues, rationing and shortages.

The obvious objection is that, if you raise prices, poor people are less able to afford the new higher prices than rich people. That is undoubtedly true. I said earlier that if prices go up, goods will tend to go to the people who need them most — and that’s true, all else being equal. But all else is not equal. Some people are simply much more able to pay.

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But it’s not clear that poor people are any better off under the current system. In the case of toilet roll, for instance, I can afford to drive to the shop in my large car and pay £50 upfront for toilet roll, so I have loads. Someone who is poorer, who has to walk or take the bus, and only has £5 or so that they can spare, will not be able to stockpile. They might have much less spare time. In the case of queuing for goods, a rich person could pay someone to queue for them, as purportedly happened in the 1973 oil embargo (there were even reports of people offering sexual favours in return for petrol), or pay for childcare while they queue themselves.

What might work better would be allowing price gouging, but subsidising goods for poorer people — people who are on housing benefit or whose children qualify for free school meals, for instance, could be given vouchers. In general, it’s a reason to aim for a more equitable society, make it so there are fewer people who can’t afford a £5 toilet roll, rather than to try to patch the problem in the moment of crisis.

There are lots of other reasons why prices might not be easy to change at short notice — why they’re “sticky” — and laws against price-gouging certainly aren’t the whole story. In the UK, as I mentioned, we don’t have price-gouging laws. But the strong social norms against price gouging have much the same effect. I spoke to someone who works in supply chain logistics for major British retail firms, and they said that it’s a brand reputation issue.

That is: you spend years building up a reputation for value and fairness, and for encouraging people to shop with you rather than elsewhere, and that reputation is worth far more to you than the quick buck you could make by jacking prices up for short-term profit.

This sounds good — firms don’t want to make a short-term profit at the cost of customer reputation! — but it has the same effect as legislation. Prices that should go up don’t, so stores don’t stock extra, people stockpile, and goods don’t end up where they’re most needed.

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It’s much harder to change public attitudes than it is to change legislation. But it’s not impossible. As Rob Wiblin, an economist and the director of research at the charity 80,000 Hours, told me, flight prices used to be fixed. Many countries deregulated their air industries in the 1970s and 1980, and now, we’re entirely used to rapidly fluctuating prices — even between two clicks on the same website. It doesn’t bother us all that much. Public attitudes are somewhat malleable.

But maybe they aren’t so malleable that we could come to accept someone charging £5 for a face mask during a pandemic — it feels immoral, at a time when we feel like we should all be coming together. Most people would probably find it disgusting, as they did when hotels increased their prices after the 7/7 bombings when thousands of people were stuck in London. But what we find disgusting is not always a good guide to what is moral, or for that matter what is effective. It might be worth changing our attitude.

Because if we can change our attitude, it would probably do us all good, especially if we combined it with sensible ways of subsidising the purchasing power of poorer people. Price-gouging has a bad name — literally — but it’s not always a bad thing. Now, if you’ll excuse me, I need to start fighting off gangs of road warriors on the streets of Haringey, because the local petrol stations haven’t price-gouged enough.

It was a Beyond burger, made from plant proteins. Back in 2017, when I tried an Impossible burger in California, they still were rare. Now you can buy them at Burger King; McDonald’s is making its own called (inevitably) the McPlant. About $7 billion dollars’ worth of vegan meat was sold in 2020 – the large majority of it replacing the traditional meat burger.

Making fake burgers takes a fraction of the energy and land that making a beef burger does and fewer cows end up dead. But, and this is crucial, I still get to eat a burger.

There was an alternative way around this, of course. Instead of developing a low-impact way of fulfilling my desire for a burger, we could have encouraged me not to want burgers any more, or (more likely) to suppress it. We could have run advert campaigns fronted by earnest celebrities telling me that burgers are bad. We could even have simply banned burgers. And maybe it would have worked.

But then I wouldn’t have got to eat a burger, and I wanted a burger.

On the whole, I think fulfilling human desires is a good thing. Not always, but most of the time, giving people things they want makes them happier. So why not?

But not everyone thinks this is a good thing. There’s a name for it: “solutionism”, the “foolhardy belief that technology can sidestep thorny social and political problems”. People who worry about solutionism say that, instead of finding technological quick fixes for society’s ills, we ought to concentrate on the root causes: to change our social structures, to change our behaviour, to change policy.

I strongly disagree. We should do both.

In the UK after the war, suicide rates went up. By the mid-Sixties, according to the national registry, they reached something like 14 per 100,000 men per year, and about 10 per 100,000 women[1. Note: this is probably an undercount. The ONS suicide data only goes back to 1981, but it starts at 19.5 per 100,000 men per year. The trend, though, is real.]. But by 1971, they had dropped significantly, to about 10 for men and seven for women. What happened? Did we make progress in solving society’s underlying problems and make Britain a more tolerable place to live?

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Well, maybe. But a more proximate explanation is that in the mid-Sixties — for reasons unrelated to suicide — the British energy supply largely switched from coal gas to natural gas.

At the time, many suicides in the UK involved carbon monoxide (CO) poisoning: putting one’s head in the oven. That became impossible with natural gas, which is essentially free of carbon monoxide. In 1962/3, about 40% of suicides used CO poisoning; by 1970/71, fewer than 10% of them did. Suicides by other means went up, a bit, but nowhere near enough to compensate; total suicide rates went down by about a third. Making it harder to kill oneself stopped a lot of people from doing it; it turned out the impulse to end their life was transient.

Suicide is a problem with many underlying causes. But a simple technological fix — changing from coal gas to natural gas — reduced the problem significantly.

How about other social ills? How about obesity? Somewhere around a quarter of British adults are classed as obese. A subset of people, about 3% of the population, are “morbidly” obese — that is, a BMI over 40, equivalent to a six-foot-tall person weighing more than 20 stone. This has serious health implications, and many of them would like to lose weight, but can’t — their brain fights them, making them feel as though they are starving. Asking them to stop eating is not that different from asking them to stop breathing.

Last week, the neuroscientist Stephan Guyenet wrote a piece about a promising pharmaceutical treatment for obesity: that is, an actually effective weight-loss pill. I thought it was exciting, but several people responding to me thought it was “dystopian” or “depressing”: people should diet and exercise. We should change society and behaviour, not rely on pills.

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But again: I think that’s just wrong. Changing society can be good! We should make cities more walkable; we should make food healthier and exercise easier, and doing so would probably reduce obesity and improve health. But for some people that won’t be enough, because not everyone has the same levels of food craving or impulse control. A pill that makes their job easier, that levels the playing field, would also be good.

There are many examples like this. The human papilloma virus (HPV) is sexually transmitted and causes cervical cancer. You can either try to encourage abstinence and safe sex (difficult) or you can vaccinate children before they reach the age of sexual activity (easy). It reduces cervical cancer by more than 90%. And people still get to have sex!

Society is really hard to change. It is made up of millions of people with their own desires and incentives, and there is limited scope for altering it — you can reduce smoking by banning it in pubs, you can reduce drink-driving through education and enforcement (drink-driving deaths are about 15% of what they were in 1979), but you’re battling against what people want.

And people getting what they want is a good thing. You could almost argue that it is the only good thing: what is the point of life, if not being happy and making others happy? If people want to smoke, and if they could do so without harming their health, what’s the problem?

So technological fixes are helpful. For example, vaping represents a way of giving people what they want — the fun of a nicotine high — at a hugely reduced health cost.

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The most obvious place where this is relevant is climate change. People want to fly, they want to drive, they want to heat their homes and use electricity. We could try to transform human desires, and make us not want those things, or we could try to make us deny our own wants. For some of us that will be successful and for others it will not.  But we could let everyone have the things they wanted, it would be better.

That’s why things like carbon capture are actually pretty exciting, things like the enormous fall in the cost of solar energy, or zero-emission aircraft. This means people getting to travel, getting to live in comfortable homes with light and heating, being able to eat food they like, with fewer negative costs.

I don’t like the tendency to compare movements, such as environmentalism, to religions — it’s too easy and is often misleading. But I do think that for some people, there’s a tendency to confuse motive and means: they think that because many of the things we want and enjoy have harmful side-effects, so the things we want and enjoy are bad in themselves. Like Puritans opposed to hunting not because the animals suffer but because the hunters enjoyed it, we think that because we sometimes need to suffer for noble purposes, that suffering itself is noble.

It’s not. We may have to change our behaviour — eat less, smoke and drink less, fly less — to gain some other good, such as improved health or reduced environmental impact. But that is an unfortunate by-product, not the point in itself. If technological fixes exist, we should use them.

Plant-based burgers are one thing; soon, hopefully, we will see real meat that’s been grown in a lab at an affordable price (although it’s always a bit further away than its proponents claim). People could order steaks or lamb chops that have never been attached to a sentient being, that don’t require acres of land or horrible cruelty. For some people this is a bleak dystopia. But for me it’s a utopian vision: people getting what they want, at a fraction of the environmental and moral cost. Let me have my damn burger.

By contrast, on Sunday I spent eight hours in a central London bar playing nerdy wargames in a tournament. When you’re surrounded by about 30 increasingly stressed men, on a warm day in a smallish, un-air-conditioned room, you become intensely aware of the limits of social distancing. I’ll find out whether I caught Covid soon enough, but the fact that it was possible to do it shows how different life is now.

There’s something worth noting, though. The current seven-day rolling average of deaths is at about 120. For context, that’s about what it was in the middle of October last year, two weeks before we went into that second lockdown. It’s about eight times what it was this time last year. As this thread from the Bristol University mathematician Oliver Johnson points out, in September 2020 the prediction that we would reach 200 deaths a day in November was met with horror and, in some cases, disbelief. But at the present rate of growth, we’ll be at 200 a day by November again, and no one seems to be all that concerned. 

Crucially, too, at that rate of growth we could end up at 2,000 hospitalisations a day — about half the January 2021 peak. If that were to happen, we probably won’t see hospitals running out of oxygen, like we did during the first two waves. But we might see it getting harder to access routine care. I spoke to one ophthalmologist earlier this year, and he said that quite a few patients suffered avoidable, but irreversible, sight loss because the sheer weight of Covid cases meant their treatment had to be delayed. That’s a microcosm of the health service in general: for comparison, 2017/18, the worst winter for excess deaths in the last half-century, saw a peak of about 1,000 hospitalisations and about 50 deaths a day, and that led to tens of thousands of hospital appointments being cancelled.

Maybe I’m misremembering, but my sense is that we were a lot more anxious last year than we are now. I don’t think there were many wargaming tournaments taking place, for one thing. Thousands of people cheered Emma Raducanu on as she won the US Open at the weekend; in 2020, it was played in front of empty stadiums.

We’re heading into winter again, and things will probably get worse — Boris Johnson is announcing plans for the coming months today — yet there remains a relatively carefree air. So why aren’t we more worried? And should we be?

There’s one obvious reason why we’re not all freaking out, and that is that we’re tired and bored and aren’t really paying attention. The news isn’t really reporting the numbers any more. 

But there are also real differences between now and last year,  mainly driven by immunity, particularly vaccines. Adam Kucharski, a mathematical epidemiologist at the London School of Hygiene and Tropical Medicine, told me that while “normalisation” has been part of it — “numbers that were massive don’t feel so massive” — there are also other things at work.

Some of it is pretty obvious. “The pool of susceptibility is very different,” he says. At the start of the pandemic, and in the second wave, there was the potential for a huge amount of devastation — “hundreds of thousands of deaths, millions of hospitalisations”. We “hit the pause button” with lockdowns and other restrictions, but that was just pushing them into the future; the potential was still there.

Now, that pool of susceptibility is much smaller. Around 90% of all over-16s in the UK are vaccinated, and about seven million people have had Covid. Those people are much less likely to die than they were a year ago. Even if we completely let Covid rip through the population, it couldn’t do as much damage. 

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This is because the vaccines work, really quite well. If you look at how deaths are distributed (p33), around 16% of Covid fatalities among the over-80s are in not-fully-vaccinated people, despite around 95% of that age group being vaccinated: having the vaccine makes you much less likely to get very ill. 

Also, the shape of the curve is very different this time around. We’re at 120 deaths a day, but the growth rate is about 7% a week. There’s this neat trick for working out compound growth: if you divide 70 by the percentage weekly growth, you get the (rough) doubling time. So it will take about 10 weeks for deaths to double. By comparison, deaths were going up 50% a week for periods in autumn and winter, doubling about every 10 days. 

That’s, again, because of vaccinations. We’re living amazingly open lives at the moment — bars and theatres open, schools open, masks not compulsory — but the numbers of cases, and hospitalisations, and deaths, have crept up rather than shot up. “It’s been at a plateau for a long time,” says Kucharski. “Last year it was a firehose; you start to open up and cases go up.” Vaccines work against infection as well, so even though we’re pretty much wide open, vaccination is keeping growth slow.

After a few months of this, we now have a sense of what living with Covid feels like. Before, there weren’t “flat” periods. Either Covid numbers were going up, as they were in spring 2020 and autumn and winter, or we’d locked down and they were declining again. There was never stability. Now, even in our quite open society, even with full football stadiums and shops, numbers are going up only slowly, because we’re less likely to get infected and less likely to get ill if we do.

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The question, of course, is what to do next. Because while the numbers may be rising slowly, they are still going up. 

Maybe that doesn’t matter and we don’t need to stop them. “The optimistic reading is that we now have so much immunity that it’ll peak on its own,” says Oliver Johnson. And there are hints that case numbers have begun to come down. But if it doesn’t, if it carries on as it is, then we’ll have 250 or so deaths a day by the middle of November, and the NHS under severe pressure. 

The three things we should have learnt about Covid by now are, one, that it is quite hard to predict but that looking at percentage growth rates and extrapolating them into the future is a good start; two, that you need to prepare for the tail risks; and three, that doing things earlier rather than later is often a good idea.

Luckily, things are still moving relatively slowly. “If it’s a car crash,” says Johnson, “it’s a very very slow-motion car crash. There are opportunities to turn it around.” 

And we probably won’t need the stringent restrictions we used in the last two waves to turn it around, because the immunity in the population has made it harder for the disease to spread. “We have different levers we can pull now,” says Kucharski. “We can keep testing, we can use vaccine certification, we can reintroduce masks or ask people to work from home.” Given the slow growth, a combination of things like those — and vaccinations for the under-16s, announced this week, and booster jabs for the elderly, which are expected to be announced soon – might be enough to slow the growth to zero, to a truly flat epidemic.

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But whatever we do, we’ll probably need to do it sooner rather than later: the Government’s bloody silly “irreversible” comment notwithstanding, introducing less stringent measures now could avoid us having to do more stringent ones later. “If hospitalisations creep up and creep up,” says Kucharski, “then if we tighten up, it could leave us with a flat epidemic at a higher plateau.” If the numbers have already doubled once or twice from where they are, that “would not leave us somewhere where we want the NHS to be over the winter”. The growth has been slower than it was, but in absolute terms, we are at a much higher level than we were this time last year, meaning that we have “less wiggle room” ahead of the winter surge in pressure on the NHS. 

Going out to restaurants, meeting people, sending my kids to school or to their gymnastics class and football — all that stuff of life is great and important. I want to have as much of it as possible over the winter. So we need to start talking about some relatively minor adjustments – stuff like bringing back masks in shops, encouraging working from home where possible, backing proper vaccination certificates. I prefer the world where I can play silly nerdy games in sweaty rooms, even if I lose all my games and come dead last, to the world where I sit on a park bench in the cold for two birthdays in a row.

She noticed something. When she interviewed the men’s rights activists, she was enraged by their sexism: but when she went back and transcribed the interviews, she found that the things she had been enraged by were not as clear as she had thought. “I would often hear an innocent and valid point that a MRA would make,” she says, “and in my head I would add on a sexist or anti-woman spin, assuming that was what he wanted to say but didn’t.”

If a man said that there are 2,000 domestic violence shelters for women in the US and only one for men, for instance, she would hear “and therefore we should defund the women’s ones”, rather than “so we should fund more men’s ones”. It’s an interesting watch.

The blogger John Nerst points out that, whether or not you agree with her, there’s something interesting going on here. Her brain was doing something incredibly sophisticated. The MRAs’ comments were being filtered, or spun, or edited, before they reached her conscious mind. They were given tags: untrustworthy, sexist, anti-woman.

As far as her consciousness was concerned, she wasn’t choosing to label them like that: they came to her pre-labelled, by some hidden subroutine in the brain. It was only when she watched it back later, and her own video diaries from the time, that she was able to spot the labels. And the labelling involves quite high-level ideas, things like “misogyny”, not just lizard-brain concepts like “danger, food, potential mate”.

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I thought of Jaye, and the strange filtering that our brains do, when I read about the probably nonexistent wave of ivermectin overdoses filling Midwestern US hospitals and preventing medics from treating gunshot victims.

This purported wave got a lot of coverage. Rolling Stone “Gunshot Victims Left Waiting as Horse Dewormer Overdoses Overwhelm Oklahoma Hospitals, Doctor Says.” The Guardian: “Oklahoma hospitals deluged by ivermectin overdoses, doctor says”. The Independent: “Doctor says gunshot victims forced to wait for treatment as Oklahoma hospitals overwhelmed by coronavirus patients.” It’s taken from a report from a local TV channel, which interviewed a rural Oklahoman emergency-room doctor.

As far as I can tell, it’s not true at all. Dr Jason McElyea, the doctor in question, appears to have been saying two things: 1) that he’s seen some ivermectin overdoses and 2) that emergency rooms are overwhelmed, presumably by Covid. The original news video splices these two points together, but I don’t think it’s what McElyea meant; in a follow-up interview with the BBC, he clarified that there was only a “handful” of overdoses, and that the strain on hospital staff was mainly from a surge in Covid cases. One Oklahoman hospital said it hadn’t seen any cases; others may have, but it makes it less likely that there’s a plague of the stuff. (It’s worth noting that Right-wing outlets didn’t cover themselves in glory either: they reported the hospital saying it hadn’t had any cases as disproving the whole story, even though McElyea had never mentioned that hospital and it was in a different bit of Oklahoma.)

To be clear, ivermectin almost certainly doesn’t help prevent or treat Covid and it can be bad for you. But it seems unlikely that there are so many people ingesting it that it’s affecting hospital capacity.

The American Association of Poison Control Centers has released data up to the end of August, showing a marked rise in ivermectin cases — about ten times as many this August as there were in August 2019. But while a tenfold rise sounds impressive, it’s only up from about 40 cases a month in 2019 to 450 a month now, and most of those cases had either no effect or minor effects. For context, about 1,600 people are being hospitalised with Covid every day in Oklahoma alone. It’s silly to take ivermectin, but people doing it are not putting huge strain on the US health service.

The ivermectin story is far from the only one recently. The Guardian among other places, recently reported a story of an “anti-trans Instagram post”, which made “unsubstantiated allegations” about a male-bodied person in a spa exposing themself to women and girls at an LA spa. The piece said experts called it “a case study in how viral misinformation can result in violence”, linking “anti-trans and far-right movements, including QAnon conspiracy theorists, who believe that a cabal of elite pedophiles is manipulating the American government”.

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There was a kind of inevitability about the follow-up story last week which found that, in fact, a registered sex offender named Darren Merager had been charged with indecent exposure at the spa. Merager, a trans woman, denies the charges; but at the very least, it’s not a clear-cut example of “viral misinformation” or a conspiracy theory worthy of being linked to QAnon.

As a bit of an aside, this isn’t supposed to happen. The Left views itself as the defender of truth and accuracy, not a purveyor of mistruth. Recently, I reviewed a book, How to Talk to a Science Denier. It was not, I felt, particularly useful. But one thing that I found revealing was the author’s long discussion of whether “left-wing science denial” existed. He chose one example — Left-wing opposition to genetically modified organisms, specifically on safety grounds — and concluded that it sort of did but not really. He based a lot of his thinking on the research of Stephan Lewandowsky, a psychologist at the University of Bristol. There is, I think, a fairly well-established idea that the Left is the “side” of science and evidence, and the Right is the “side” of science “denial”.

I find that strange, because I can think of quite a few areas in which elite Left-wing or liberal opinion doesn’t sit well with mainstream scientific findings.

For instance: the UK Green Party wants to “phase out” nuclear power. Is that “science denial”? I don’t know, but I think the consensus scientific position is that nuclear power is extremely safe and carbon-efficient.

And the idea that human behaviour and society are in some important way the product of evolution has been so profoundly uncomfortable to people on the Left that as far back as 1978 people broke into a lecture the biologist Edward Wilson, shouting “Racist Wilson, you can’t hide, we charge you with genocide” and throwing water over him, after he had written a book, Sociobiology, which was mainly about the behaviour of insects but which speculated that future science could shed some light on human behaviour. Is that “science denial”? Well, again, that’s a question of definitions, but blank slatism is certainly not directly in line with modern scientific findings.

Or take IQ. Your score on an IQ test predicts your success in life pretty well; your career outcomes and earnings, your school results, even your lifespan. It’s also highly heritable. You can find people denying those facts on the Left much more easily than you can find them on the Right, just as you can find people claiming that biological sex isn’t real.

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My point isn’t that Left-wing people do this more than Right-wing people, or even that they’re equivalent: I suspect that if you could find some reasonable way of defining terms like “science denial” or “misinformation” (and “Right-wing” and “Left-wing”), you’d probably end up finding that it’s more prevalent on the Right. But, at least in the largely Left-wing or centrist circles I inhabit, it’s almost axiomatic that misinformation is what the Right do. There’s a serious strain of thought that thinks we are the correct ones, and they are the wrong ones.

But the desire to show your political opponents in the worst possible light, or to believe things that are politically convenient rather than things that aren’t, is entirely bipartisan. It’s just easier to spot when the enemy is doing it.

But what interests me is the question of whether people (on the Left or Right) do this on purpose: are they consciously misrepresenting their opponents and ignoring inconvenient truths, or is it a subconscious, automatic thing?

I wrote some months ago about people switching between different definitions of words in highly charged culture-war debates, allowing them to make claims like “cancel culture is/isn’t real”. I’d assumed it was mainly unconscious, but something that people said to me was: this is entirely deliberate. They define words in ways that gain them cultural power: so a Left-winger might very deliberately define “racism” to include “anything that leads to negative outcomes for non-white people”, and a Right-winger might define it as “only explicit acts of intentional racism by an individual”, so as to make the problem as big or as small as you can. Certainly it seems to happen too often for it to be accidental.

Under that model, people share the ivermectin story or the trans-woman-in-the-spa story to damage their political opponents, knowing or not caring that it’s not true. Ivermectin is a Right-coded thing, so showing that Trump-voting rural Oklahomans are taking it in their thousands and gumming up emergency wards shows just how stupid they are.

But the Cassie Jaye MRA thing, I think, reveals another angle. As Nerst describes it, Jaye’s conscious brain wasn’t choosing the most uncharitable interpretations of what the MRAs were saying: her subconscious brain only presented her with uncharitable options. Her conscious, reasoning mind, the bit of us that we consider ourselves, thought it was simply reporting reality, but in fact the version of reality that arrived in front of it had already been screened, and only the politically convenient bits were available to her. Nerst calls this process of only choosing from pre-screened options “semitentionality”: it’s neither an honest mistake nor a cynical misrepresentation, but some weird different third option.

I think that’s what’s going on when people read stories about ivermectin or trans women in spas. It’s not that they deliberately choose the interpretation most convenient to their pre-existing beliefs, or that they’re accidentally choosing the most convenient interpretation time and time again. It’s that the world our brains present to us is brilliantly constructed so that our friends are right and good and the enemy is bad and wrong. It’s not clear how we can improve the situation — but being wary of sharing any politically convenient stories on social media might be a good start.