When the best pandemic models are the simplest

As a pandemic of coronavirus spreads across the globe, people debate 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. 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 outcomes of different policy strategies. 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.

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. But, for each added complexity, a modeler may have to choose additional parameter values for which there is often little rationale but that can have a big impact on predictions. Our simulations suggest that the added complexity offers little predictive advantage.

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