Abstract
The extent and duration of immunity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods for alleviating biases in these studies is important for informing their design and analysis. Confounding by individual-level risk factors in observational studies like these is relatively well appreciated. Here, we show how geographic structure and the underlying, natural dynamics of epidemics can also induce noncausal associations. We take the approach of simulating serological studies in the context of an uncontrolled or controlled epidemic, under different assumptions about whether prior infection does or does not protect an individual against subsequent infection, and using various designs and analytical approaches to analyze the simulated data. We find that in studies assessing whether seropositivity confers protection against future infection, comparing seropositive persons with seronegative persons with similar time-dependent patterns of exposure to infection by stratifying or matching on geographic location and time of enrollment is essential in order to prevent bias.