Using Serosurveys to Optimize Surveillance for Zoonotic Pathogens.

Abstract

Zoonotic pathogens pose significant risk to human health, with spillover into human populations contributing to chronic disease and epidemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs remains incomplete. This challenge is compounded when prevalence in reservoir populations reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections or predict when spillover risk is likely to be greatest. Thus, we develop a general mathematical model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens. Using simulated data, we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. Then, we demonstrate an implementation of our method using previously published surveillance data in straw-colored fruit bats (Eidolon helvum). The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to cyclic, but potentially short-lived, pulses of pathogen prevalence.