Covariation between the physiological and behavioral components of pathogen transmission: Host heterogeneity determines epidemic outcomes
10 October 2017White, Lauren; Forester, James; Craft, Meggan
Although heterogeneity in contact rate, physiology, and behavioral response to infection have all been empirically demonstrated in host-pathogen systems, little is known about how interactions between individual variation in behavior and physiology scale-up to affect pathogen 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 host populations. We tested the consequences of contact rate covarying with susceptibility, infectiousness, and infection status using an individual-based, dynamic network model where individuals initiate and terminate contacts with conspecifics based on their behavioral predispositions and their infection status. Our results suggest that both heterogeneity in physiology and subsequent covariation of physiology with contact rate could 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; (2) when contact rate and susceptibility or infectiousness negatively covary, it takes substantially longer for epidemics to spread throughout the population, and rates of epidemic spread remained suppressed even for highly transmissible pathogens; 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 transmissibility and for populations where the observed variation in contact rate was higher, suggesting that such heterogeneity may be most important for less infectious, more chronic diseases in wildlife. Understanding when and how variability in pathogen transmission should be modelled is a crucial next step for disease ecology.