Meeting Abstract
Infections in most animal species cause an antibody response that can be captured with a model of within-host antibody kinetics. Individual-level variation in the deterministic infection curve can be quantified using antibody quantity data from individuals sampled over time with known infection times. This variation and prediction of the infection curve then can be used to infer time of infection in serosurveillance samples collected once from individuals with unknown times of infection. With estimates of infections times, it is possible to derive important population-level metrics for risk assessment such as force of infection – the rate that susceptible individuals become infected – and understand how risk changes in time. We apply this approach to convenience samples of antibodies to influenza A in feral swine to determine seasonality in disease risk and spatial spreading patterns. We compare our individual-based method of inferring population-level processes to risk assessment methods that use seroprevalence analyses (a common method of assessing spatio-temporal patterns of risk using serosurveillance data). Inference of seasonal risk dynamics and spatial spreading using the individual-based approach differ from seroprevalence analyses. The seroprevalence patterns are tightly coupled to the sampling design, but our individual-level approach allows for inference of spatio-temporal risk patterns that differ from the sampling design. Our analyses demonstrate a method for improving risk assessment in wildlife disease surveillance programs, and can be used to improve our understanding of the role of individual-level variation in driving disease dynamics.
