Using changes in host demographic rates to reveal the effects of infection with hidden variable models

Abstract

15The impacts of disease on host vital rates can be clearly demonstrated using longitudinal 16 studies, but these studies can be expensive and logistically challenging. We examined the 17 utility of hidden variable models to infer the individual effects of disease, caused by 18 infection, from population-level measurements of survival and fecundity when 19 longitudinal studies are not possible. Our approach seeks to explain temporal changes in 20 population-level vital rates by coupling observed changes in the infection status of 21 individuals to an epidemiological model. We tested the approach using both single and 22 coinfection viral challenge experiments on populations of fruit flies (Drosophila 23 melanogaster). Specifically, we determined whether our approach yielded reliable 24 estimates of disease prevalence and of the effects of disease on survival and fecundity 25 rates for treatments of single infections and coinfection. We found two conditions are 26 necessary for reliable estimation. First, diseases must drive detectable changes in vital 27 rates, and second, there must be substantial variation in the degree of prevalence over 28 time. This approach could prove useful for detecting epidemics from public health data in 29 regions where standard surveillance techniques are not available, and in the study of 30 epidemics in wildlife populations, where longitudinal studies can be especially difficult to 31 implement. 32 2