Transmission heterogeneity among individuals is a key element in infectious disease dynamics. Failure to account for such heterogeneity has potentially dangerous or expensive consequences for modelling outbreak probabilities and forecasting continued spread of disease. Generally, transmission heterogeneity cannot be determined a priori using individual-level traits, and instead, relies on extensive molecular analyses. Acquiring these data can be costly and time-intensive, especially for studies of disease dynamics in wildlife. Consequently, we asked whether individual-level traits can predict viral shedding in bats. To answer this question, we collected oral swabs from 1,086 wild bats from Puerto Rico, and recorded six individual-level host traits: mass, forearm length, ectoparasite intensity, sex, and reproductive status. We then tested the swabs twice for Herpesvirus infection and developed a Bayesian community-level occupancy model that simultaneously estimated the probability of infection and the probability of viral detection. For this model, we included bacterial load and viral subfamily as fixed effects influencing detection probability. Host forearm length, roosting cave, ectoparasite intensity, sex, reproductive status, and the interaction between sex and reproductive status were included as fixed effects driving infection. Additionally, we included host species as a random variable for predictions of detection and infection.
Results/Conclusions
We collected 1,086 oral swabs from eight bat species in Puerto Rico. From these samples, we detected 41 unique Herpesviruses. Viral detection with only a single PCR assay was low. However, running two assays and using an occupancy model to account for imperfect detection significantly improved results – a methodological advancement in analysis of disease dynamics. We found that Herpesvirus richness was significantly higher in reproductively active female bats, compared to non-reproductive bats or reproductively-active male bats. No other host traits significantly affected the probability of viral shedding, and neither bacterial load nor viral subfamily significantly affected viral detection. These results suggest that inclusion of host reproductive status can improve quantitative assessments of disease dynamics. Results also indicate that disease risk to humans is higher when bats are reproducing. This is a potential win-win for bat conservation and human health, as minimizing disturbance during periods of bat reproduction would lower spillover risk and facilitate population growth.