Professor Michael Levitt is not an epidemiologist. He’s Professor of Structural Biology at the Stanford School of Medicine, and winner of the 2013 Nobel Prize for Chemistry for “the development of multiscale models for complex chemical systems.” With a purely statistical perspective, he has been playing close attention to the Covid-19 pandemic since January. Freddie Sayers writes in Unherd that Levitt’s observation is a simple one: that in outbreak after outbreak of this disease, a similar mathematical pattern is observable regardless of government interventions. After around a two week exponential growth of cases (and, subsequently, deaths) some kind of break kicks in, and growth starts slowing down. The curve quickly becomes “sub-exponential.” This may seem like a technical distinction, but its implications are profound. The famous model from Imperial College — with a consistent R number of significantly above 1 and a consistent death rate – persuaded governments to take drastic action. But Professor Levitt’s point is that that hasn’t actually happened anywhere, even in countries that have been relatively lax in their responses.