Unjustified Poisson assumptions lead to overconfident estimates of the effective reproductive number.

Time-varying effective reproductive numbers of infectious diseases are commonly estimated using renewal equation models. In the widely applied R package EpiEstim and various related tools, this approach is combined with a Poisson distributional assumption. This has been criticized on various occasions, mostly on grounds of general model realism or a desire to estimate dispersion parameters. Here, we argue that an important issue arising from the Poisson assumption is that inference about the effective reproductive number becomes overconfident in the presence of overdispersion. The extent to which standard errors are underestimated follows from theory on generalized linear models in a straightforward manner. We therefore recommend to replace the Poisson assumption by quasi-Poisson or negative binomial extensions, and contrast their respective properties. We illustrate our arguments in detailed simulation studies and three case studies on Ebola, pandemic influenza, and COVID-19.