Rapid Assistance in Modelling the Pandemic. How can non-specialists help with 'the science'.
When the pandemic hit, the reaction of researchers around the world was "How can I help"? The consequence was over 10,000 well-meaning but unreviewed preprint papers and hundreds of amateurs cluttering the inboxes of the government's scientific advisors.
There had to be a better way.
The Royal Society sent an appeal to the computer modelling community to help the epidemiologists. Rapid Assistance to Modelling the Pandemic: RAMP was then born. Soon dozens of coders were seconded to the leading modelling groups, contributing to the signal, not the noise. We rethought how to do peer review in a crisis? triage: crowdsourced reviews of the preprints, then a rapid filtering to an expert review panel, and onwards to the UK government advisory committees SPI-M and SAGE.
Prof Ackland and RAMP
Graeme Ackland's role within RAMP was the crowdsourced reviews and 'leading new research': partly a polite shorthand for 'shielding the existing experts'. With rapid turnaround required by policymakers, even the best groups are in danger of slip ups, so another RAMP task was replication of influential work. We looked at the code for the model of Imperial College's Neil Ferguson. His group's blandly-titled 'Report 9' predicted half a million deaths if nothing was done, and is generally regarded as 'the science’ behind the lockdown. Reading it carefully, we noticed something odd: while all the proposed measures slowed the epidemic, school closures increased the total number of deaths. Our first thought was that it was a mistake, but after a little work on the code, we replicated the result. The basic reason for this counter-intuitive result is that an intervention that substantially suppresses the first-wave epidemic leads to a stronger second wave once the interventions are lifted.
Scientific predictions are often published to great fanfare, and publicised by whichever section of the media whose prejudices they happen to support, with retrospective analysis seldom attracting as much attention. Report 9 appeared in March, we completed our study in June, and a lot has happened since. Infections went down steadily during lockdown, as predicted, and at the time of writing are rising again, just as predicted. With hindsight, the Imperial model has proved remarkably accurate, whereas the enthusiastic amateurs have fared less well. It turned out that some of the experts really are expert.
Trigger Warning: Graeme is 58 years old and in a fairly high risk category, and has spent several months poring over graphs with Death on the y-axis. Possible consequences of this are depression or lack of sensitivity. Graeme is not depressed.
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