Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease. � 2017 John Wiley & Sons Ltd/CNRS
National Wildlife Research Center, United States Department of Agriculture, 4101 Laporte Ave, Fort Collins, CO, United States; Department of Biology, Colorado State University, Fort Collins, CO, United States; Animal and Plant Health Inspection Service, United States Department of Agriculture, Veterinary Services, 2155 Center Drive, Building B, Fort Collins, CO, United States; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States; MRC Centre for Outbreak Analysis and Modelling, Imperial College, London, United Kingdom; U.S. Geological Survey, Northern Rocky Mountain Science Center, 2327 University Way, Bozeman, MT, United States; U. S. Geological Survey, Wisconsin Cooperative Wildlife Research Unit, University of Wisconsin, 1630 Linden Drove, Madison, WI, United States; U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, 1484 Campus Delivery, Fort Collins, CO, United States; Departments of Fish, Wildlife & Conservation Biology and Statistics, Colorado State University, 1484 Campus Delivery, Fort Collins, CO, United States; Department of Statistics, Colorado State University, Fort Collins, CO, United States; Department of Ecology & Evolutionary Biology, UCLA, Los Angeles, CA, United States
Recommended Citation:
Pepin K.M.,Kay S.L.,Golas B.D.,et al. Inferring infection hazard in wildlife populations by linking data across individual and population scales[J]. Ecology Letters,2017-01-01,20(3)