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