Offhand I’m going to venture a guess that John P.A. Ioannidis, Sally Cripps, and Martin A. Tanner will make very few friends, particularly among policymakers, with their critique of forecasting models for COVID-19, published at the site of the International Institute of Forecasters. Here’s their conclusion:
Blindly acting based on extreme value theory alone would be sensible if we lived in the times of the Antonine plague or even in 1890, with no science to identify the pathogen, elucidate its true prevalence, estimate accurately its lethality, and carry out good epidemiology to identify which people and settings are at risk. Until we accrue this information, immediate better-safe-than-sorry responses are legitimate, trusting extreme forecasts as possible (not necessarily likely) scenarios. However, caveats of these forecasts should not be ignored [1,18] and new evidence on the ground truth needs continuous reassessment. Upon acquiring solid evidence about the epidemiological features of new outbreaks, implausible, exaggerated forecasts [19] should be abandoned. Otherwise, they may cause more harm than the virus itself.
I see some problems with that statement not the least of which is that we don’t know for sure even more than three months after the World Health Organization declared the pandemic that we’re not living, effectively, “in the times of the Antonine plague or even in 1890”. We don’t have a cure; we don’t have a vaccine; we don’t even have really effective treatments; we don’t know whether the ability to identify the virus will lead to any of those things; we haven’t carried out “good epidemiology to identify which people and settings are at risk”. That last is something I have been encouraging for months. A key problem is that the ethical obligations of physicians, prime among which is to care for their patients, can be in conflict with the welfare of the population as a whole.
I found this passage particularly gratifying:
Many models assume homogeneity, i.e. all people having equal chances of mixing with each other and infecting each other. This is an untenable assumption and in reality, tremendous heterogeneity of exposures and mixing is likely to be the norm. Unless this heterogeneity is recognized, estimated of the proportion of people eventually infected before reaching herd immunity can be markedly inflated
since that is a point I have been hammering for months.
I think another basic problem is that science just doesn’t work the way people want it to and, particularly, it is peculiarly unamenable to be carried on in social media.
“that we’re not living, effectively, “in the times of the Antonine plague or even in 1890â€. We don’t have a cure; we don’t have a vaccine; we don’t even have really effective treatments”
But we arent living in those times. 5% of those between 50-64 (from memory so may be off a bit) needed hospitalization. They received non invasive oxygen, treatment for secondary infections and treatment for BP changes and anticoagulants to stop the clotting complications. None of that happens in 1890. A large percentage of those in that 5% die have an MI or suffer a stroke.
“tremendous heterogeneity of exposures and mixing is likely to be the norm.”
I dont know about tremendous, but I think we will find a lot. That is one of the reasons I like the idea of the super spreaders. It explains a lot of the variability.
“it is peculiarly unamenable to be carried on in social media.”
I would agree with this in the sense that I am pretty sure you mean. That said, when you have twitter groups or social media groups of some ilk composed only of scientists, or in my case physicians, it can actually be pretty helpful. It is a time efficient, very fast way to spread information.
Steve