The importance of model assumptions in estimating the dynamics of the COVID-19 epidemic

Bénéteau et al. investigate the estimations by several models of the dates of the beginning and the end of the SARS-CoV-2 epidemic in France. This is a difficult problem as the number of infected people on both tails of the epidemic is low, meaning that assumptions at the heart of commonly-used SIR-based deterministic models become inappropriate. They propose a new stochastic model, a version of which includes superspreaders, and compare the estimates of this model to a deterministic SIR-like model and to another published deterministic model that includes age stratification. They find that estimates of the end of the epidemic following lockdowns are more sensitive to the assumptions of the models used than estimates of its beginning.