As the coronavirus pandemic spreads across the globe, people are debating policies to mitigate its severity Many complex, highly detailed models have been developed to help policy setters make better decisions However, the basis of these models is unlikely to be understood by non-experts We describe the advantages of simple models for COVID-19 We say a model is "simple" if its only parameter is the rate of contact between people in the population This contact rate can vary over time, depending on choices by policy setters Such models can be understood by a broad audience, and thus can be helpful in explaining the policy decisions to the public They can be used to evaluate the outcomes of different policies However, simple models have a disadvantage when dealing with inhomogeneous populations To augment the power of a simple model to evaluate complicated situations, we add what we call "satellite" equations that do not change the original model For example, with the help of a satellite equation, one could know what his/her chance is of remaining uninfected through the end of an epidemic Satellite equations can model the effects of the epidemic on high-risk individuals, death rates, and nursing homes and other isolated populations To compare simple models with complex models, we introduce our "slightly complex" Model J We find the conclusions of simple and complex models can be quite similar However, for each added complexity, a modeler may have to choose additional parameter values describing who will infect whom under what conditions, choices for which there is often little rationale but that can have big impacts on predictions Our simulations suggest that the added complexity offers little predictive advantage
When the Best Pandemic Models are the Simplest