Bat-borne zoonoses pose an ongoing threat to human health. Understanding mechanisms behind pathogen transmission, maintenance and spillover is crucial for effective surveillance and control. Evidence suggests that many bat-coronavirus systems exhibit regular seasonal viral shedding, and that reproductive phenology and waning maternal immunity generate these patterns. However, it remains unclear whether these factors alone explain the observed seasonality, and why some bat-virus systems with similar reproductive phenology exhibit more complex dynamics. Here, Bayesian methods were used to calibrate four seasonally forced mechanistic models of coronavirus dynamics in a Cambodian fruit bat (Pteropus lylei) population. Model selection identified the best predictive model, which reliably reproduced seasonality in data from Thailand. Additionally, Markov chain Monte Carlo was used to explore how complexity in disease dynamics varies across parameter space. Our findings indicate that (i) bat reproductive phenology and CoV specific transmission and immunity parameters are sufficient to generate regular annual peaks in CoV shedding, and (ii) more complex patterns were only detected to occur outside the posterior and prior distributions of the selected model. These results provide important insights for long term zoonotic surveillance.
Yu et al. (Wed,) studied this question.