Thank you, Tom, for this helpful exposition. There is an inevitable tendency for people to look for simple answers to complex questions, not helped by our media being dominated by Arts graduates.
Rouben TER-MINASSIAN
4 years ago
Thanks for this work and the others ones you posted. I may say you plainly deserve to be called a science writer. Any hunch of why you are so fiew ? incompatibility with journalism ?
T J Putnam
4 years ago
Nicely argued apart from the conclusion that comparisons are too complex to be of much value. On the contrary, it’s comparisons that highlight critical factors for the further investigation that’s key to progress. For example, France has less collateral health care damage and care home morbidity than UK. Why? Is it because France separated Covid19 from other hospital treatments and isolated their care homes better (which they did) or are there other reasons?
Adrian Smith
4 years ago
The real issue is that R is not a precisely calculated fact, but is more like an educated guess based on imperfect measurements fed into imperfect calculations, which are then blobbed together in a manner which is not free from bias – in many ways it is like the educated guess that gives us global temperature graphs that prop up global warming theory, which are also then presented as fact.
Jeremy Stone
4 years ago
Covd-19 seems to be a case where Simpson’s Paradox may not be the explanation for counter-intuitive results. Assuming 21 days from infection to death (in cases where death is the outcome), peak infection occurred a few days before the lockdown (as observed by Professor Heneghan). The period of steepest decline then came over the next 10 days or so, prior to the earliest point at which the lockdown could have taken effect. From the peak to this “lockdown point” Rt must have been negative (else the numbers infected would at least have been flat. From the lockdown point to mid April. the rate of decline flattened (and the trend line has an r-squared of pretty much nothing). Now this could be a result of the lagged sub-epidemic in care homes, but if so the flattening out of the fall in Rt would not be an instance of R in care homes also falling. It would just be the effect of adding the two populations together, with their different phases giving the overall distribution a secondary peak (or hump). The failure of lockdown as a policy is two-fold, in failing to prevent the second peak and being irrelevant to the decline that followed the first peak.
Thank you, Tom, for this helpful exposition. There is an inevitable tendency for people to look for simple answers to complex questions, not helped by our media being dominated by Arts graduates.
Thanks for this work and the others ones you posted. I may say you plainly deserve to be called a science writer. Any hunch of why you are so fiew ? incompatibility with journalism ?
Nicely argued apart from the conclusion that comparisons are too complex to be of much value. On the contrary, it’s comparisons that highlight critical factors for the further investigation that’s key to progress. For example, France has less collateral health care damage and care home morbidity than UK. Why? Is it because France separated Covid19 from other hospital treatments and isolated their care homes better (which they did) or are there other reasons?
The real issue is that R is not a precisely calculated fact, but is more like an educated guess based on imperfect measurements fed into imperfect calculations, which are then blobbed together in a manner which is not free from bias – in many ways it is like the educated guess that gives us global temperature graphs that prop up global warming theory, which are also then presented as fact.
Covd-19 seems to be a case where Simpson’s Paradox may not be the explanation for counter-intuitive results. Assuming 21 days from infection to death (in cases where death is the outcome), peak infection occurred a few days before the lockdown (as observed by Professor Heneghan). The period of steepest decline then came over the next 10 days or so, prior to the earliest point at which the lockdown could have taken effect. From the peak to this “lockdown point” Rt must have been negative (else the numbers infected would at least have been flat. From the lockdown point to mid April. the rate of decline flattened (and the trend line has an r-squared of pretty much nothing). Now this could be a result of the lagged sub-epidemic in care homes, but if so the flattening out of the fall in Rt would not be an instance of R in care homes also falling. It would just be the effect of adding the two populations together, with their different phases giving the overall distribution a secondary peak (or hump). The failure of lockdown as a policy is two-fold, in failing to prevent the second peak and being irrelevant to the decline that followed the first peak.