Successful pathogen transmission is contingent upon individual behavior that leads to contact between infectious and susceptible hosts, and the transmission efficiency of the agent, which depends on the physiology of both the pathogen and host. Although heterogeneity in contact rate, physiology, and behavioral response to infection have all been demonstrated empirically in wildlife host-pathogen systems, little is known about how interactions between individual variation in behavior and physiology scale-up to affect transmission at a population level. The objective of this study is to evaluate how covariation between the behavioral and physiological components of transmission might affect epidemic outcomes in wildlife populations. Here, we developed an individual-based, dynamic network model where individuals initiate and terminate contacts with conspecifics based on their individual behavior predispositions and their infection status. We then tested how contact rate covaries with susceptibility, infectiousness, and infection status.
Accounting for contact heterogeneity has already been shown to dramatically alter disease predictions; however, our results support the idea that both heterogeneity in physiology and subsequent covariation of physiology with contact rate could also powerfully influence epidemic dynamics. Overall, we found that: (1) individual variability in susceptibility and infectiousness can reduce the expected maximum prevalence and increase epidemic variability respectively; (2) when contact rate and susceptibility or infectiousness negatively covary, it takes substantially longer for epidemics to spread throughout the population; and (3) reductions in contact rate resulting from infection-induced behavioral changes can prevent the pathogen from reaching most of the population. These effects were strongest for theoretical pathogens with lower infection probabilities and for populations where the observed differences in mean degree were greater, suggesting that such heterogeneity may be most important for less infectious, more chronic diseases in wildlife, such as bovine tuberculosis. Understanding how and when variability in pathogen transmission should be modelled is a crucial next step for the field of disease ecology; moreover, such insight is a critical refinement for modeling strategies that address the growing global threats of zoonoses and emerging infectious diseases.