Governments cannot openly admit that the “controlled easing” of COVID-19 lockdowns in fact means controlled progress toward so-called herd immunity to the virus. Much better, then, to pursue this objective silently, under a cloud of obfuscation, and hope that a vaccine will arrive before most of the population gets infected.
LONDON – The COVID-19 pandemic is the first major global crisis in human history to be treated as a mathematical problem, with governments regarding policy as the solution to a set of differential equations. Excluding a few outliers – including, of course, US President Donald Trump – most political leaders have slavishly deferred to “the science” in tackling the virus. The clearest example of this was the UK government’s sudden shift on March 23 to an aggressive lockdown policy, following a nightmarish forecast by Imperial College London researchers of up to 550,000 deaths if nothing was done to combat the pandemic.
Such modeling is the correct scientific approach when the question debars experiment. You can test a new drug by subjecting two groups of lab rats to identical conditions, except for the drug they are given, or by administering it to randomly selected humans in clinical trials.
But you can’t deliberately insert a virus into a human population to test its effects, although some Nazi concentration-camp doctors did just that. Instead, scientists use their knowledge of the infectious pathogen to model a disease’s pattern of contagion, and then work out which policy interventions will modify it.
Predictive modeling was first developed for malaria over a century ago by an almost-forgotten English doctor, Ronald Ross. In a fascinating 2020 book, the mathematician and epidemiologist Adam Kucharski showed how Ross first identified the mosquito as the infectious agent through experiments on birds. From this fact, he developed a predictive model of malaria transmission, which was later generalized as the SIR (Susceptible, Infected, and Recovered) model of contagious-disease epidemics.
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