Epidemics of infectious pathogens degrade host health and drive populations to extinction or endangerment. Thus it is in the public interest to predict epidemic severity. One complicating factor to this is that epidemics generally occur in coinfected host populations, and inter-specific within-host interactions scale up to alter disease transmission and prevalence. Further, within-host interactions themselves change annually due to within-host priority effects, as shifting epidemic start dates alter the order that pathogens arrive in coinfected hosts. However, by measuring parasite interactions in the lab, we may be able to predict epidemic severity in coinfected populations. Thus we ask the following questions: (1) How do coinfection and epidemic phenology interact to shape epidemic severity, and (2) can we predict shifts in epidemic severity by measuring within-host interactions in a lab setting. We answer these questions by measuring within host interactions and within host priority effects in zooplankton coinfected by fungal and bacterial pathogens, incorporating those interactions into epidemic models, and comparing model predictions to experimental multi-pathogen epidemics.
Results/Conclusions
We found that coinfection reduced the severity of bacterial epidemics, but did so most dramatically when the bacterial epidemic start date preceded the fungal epidemic start date. Fungal epidemic severity was unaffected by coinfection. We found that qualitative patterns of how coinfection and epidemic timing impacted epidemic severity was predicted by epidemic models which incorporated the sign, but not magnitude of within host interactions and priority effects measured in the lab. This indicates that shifting timing of epidemic will alter epidemic severity in coinfected systems, but that we have tools available to predict these changes. However, the magnitude of pathogen interactions may change between lab and field settings, and we need a framework to predict these changes.