Abstract
Author summaryEffectively monitoring disease outbreaks requires determining the proportion of diseased individuals and the consequences of disease through time. The ecological fallacy is the statistical assumption that groups are homogenous when they are not. The fallacy can arise in epidemiology when studying populations composed of both diseased and healthy individuals. The average mortality in the population will not accurately describe either a diseased or a healthy individual. This is a challenge for monitoring disease outbreaks since many forms of monitoring data are collected at the population level. Here, we apply a modeling approach that accounts for disease transmission at the individual-level when data are collected at the population-level. The approach works by comparing the pattern of mortality through time in a population with a disease to a fully healthy one. The observed rate of change in mortality through time informs the model of the infection process. We validated our approach using an experimental system (Drosophila melanogaster infected with different viral pathogens). We then applied the method to study an outbreak of phocine distemper in harbor seals. Our results show that the individual-level effects of disease can be determined from population-level data.
The impacts of disease on host vital rates can be 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 infectious disease from population-level measurements of survival when longitudinal studies are not possible. Our approach seeks to explain temporal deviations in population-level survival after introducing a disease causative agent when disease prevalence cannot be directly measured by coupling survival and epidemiological models. We tested this approach using an experimental host system (Drosophila melanogaster) with multiple distinct pathogens to validate the ability of the hidden variable model to infer per-capita disease rates. We then applied the approach to a disease outbreak in harbor seals (Phoca vituline) that had data on observed strandings but no epidemiological data. We found that our hidden variable modeling approach could successfully detect the per-capita effects of disease from monitored survival rates in both the experimental and wild populations. Our approach may 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.