More Details on Pandemic Modelling

Earlier this week, I posted about an article that explained some of the difficulties involved in modelling the COVID-19 pandemic. Nature, one of the major scientific journals, has just published an article that goes into even more detail.

Governments across the world are relying on mathematical projections to help guide decisions in this pandemic. Computer simulations account for only a fraction of the data analyses that modelling teams have performed in the crisis, Ferguson notes, but they are an increasingly important part of policymaking. But, as he and other modellers warn, much information about how SARS-CoV-2 spreads is still unknown and must be estimated or assumed — and that limits the precision of forecasts. An earlier version of the Imperial model, for instance, estimated that SARS-CoV-2 would be about as severe as influenza in necessitating the hospitalization of those infected. That turned out to be incorrect.

The true performance of simulations in this pandemic might become clear only months or years from now. But to understand the value of COVID-19 models, it’s crucial to know how they are made and the assumptions on which they are built. “We’re building simplified representations of reality. Models are not crystal balls,” Ferguson says.