Proceedings of the Royal Society B: Biological Sciences
The ability to rapidly detect and respond to wildlife morbidity and mortality events is critical for reducing threats to wildlife populations. Surveillance systems that use pre-diagnostic clinical data can contribute to the early detection of wildlife morbidities caused by a multitude of threats, including disease and anthropogenic disturbances. Here, we demonstrate proof of concept for use of a wildlife disease surveillance system, the 'Wildlife Morbidity and Mortality Event Alert System', that integrates pre-diagnostic clinical data in near real-time from a network of wildlife rehabilitation organizations, for early and enhanced detection of unusual wildlife morbidity and mortality events. The system classifies clinical pre-diagnostic data into relevant clinical classifications based on a natural language processing algorithm, generating alerts when more than the expected number of cases is recorded across the rehabilitation network. We demonstrated the effectiveness and efficiency of the system in alerting to events associated with both common and emerging diseases. Tapping into this readily available unconventional general surveillance data stream offers added value to existing wildlife disease surveillance programmes through a relatively efficient, low-cost strategy for the early detection of threats.
early detection system, general disease surveillance, wildlife disease surveillance, wildlife morbidity, wildlife mortality, wildlife rehabilitation
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This work is licensed under a Creative Commons Attribution 4.0 License.
Moss Landing Marine Laboratories
Terra R. Kelly, Pranav S. Pandit, Nicole Carion, Devin F. Dombrowski, Krysta H. Rogers, Stella C. McMillin, Deana L. Clifford, Anthony Riberi, Michael H. Ziccardi, Erica L. Donnelly-Greenan, and Christine K. Johnson. "Early detection of wildlife morbidity and mortality through an event-based surveillance system" Proceedings of the Royal Society B: Biological Sciences (2021). https://doi.org/10.1098/rspb.2021.0974