A simulation-based approach to strengthen chronic wasting disease surveillance in captive cervid populations

Abstract

Captive cervid facilities are at elevated risk for chronic wasting disease (CWD) due to high deer densities, close animal contact, environmental contamination, and frequent movement of deer between facilities and across regions. Once CWD becomes established, it spreads quickly and is nearly impossible to eliminate, making early detection critical. However, surveillance in captive herds is challenging: testing only a small portion of the herd during the early stages of CWD outbreak provides a low likelihood of detecting infected deer. Moreover, not detecting CWD in a small sample cannot be interpreted as proof that the facility is free of CWD unless the entire herd is tested using a diagnostic test with 100% sensitivity. We developed a simulation-based approach to interpret surveillance results when CWD is not detected in the sampled subset of animals within captive cervid facilities. This simulation-based approach is retrospective in nature. By integrating deer demographic data, individual movement histories, and past CWD testing records, this tool estimates facility-level CWD detection probabilities, providing a clearer assessment of the likelihood of undetected disease within a facility. Using data from 23 captive cervid facilities in Texas, we demonstrate how this tool can support risk-based monitoring and help wildlife agencies prioritize surveillance and management efforts where they are most needed.