COS 84-3 - Interagency teamwork: Leveraging big data science in wildlife health

Background/Question/Methods

Recently, emerging techniques in big data science have been applied with success to advance knowledge in fields ranging from economics to finances to politics. The need in ecological studies for an analytical change in thinking from individual state agencies or researchers using traditional statistics to large interstate collaborations using techniques in big data science is abundantly clear when considering a wildlife disease. Diseases operate in free-ranging wildlife in manners that are independent of cadastral boundaries and inter-agency teamwork is key in combatting this shared threat to our public trust resources. We hypothesized that interagency collaboration could engender success in big data science to advance studies in wildlife health. Since our collaboration between the Wildlife Health Lab at Cornell University and the Boone and Crockett Quantitative Wildlife Center at Michigan State University began in 2019, members of the Surveillance Optimization Project for Chronic Wasting Disease (“SOP4CWD”) have used interdisciplinary discourse to grow the collaboration to include additional academic, federal, and industry research partners.

Results/Conclusions

We have collected nearly a million data points through the wildlife agencies of 23 US states and 1 Canadian province, creating the opportunity to leverage mathematical modeling and big data science techniques, such as algorithms, to provide answers to nagging wildlife health questions that may be too subtle for a single agency to answer using its own data. The project resulted in a Warehouse for agencies to upload and store data that contains pre-programmed models used to analyze that data. These results are then viewed in an online interactive interface, all hosted in perpetuity on a non-profit organizational website. Online tools have been made available to all participating agency professionals to improve their surveillance effectiveness, minimize the cost of their annual sampling, and - through patterns extracted from regional and local data - maximize the probability of discovering new infections. While SOP4CWD is aimed at combatting CWD in cervid populations, we conclude that this project is an example of a successful collaborative technique that can usher in a future for studies in wildlife health that meshes big data science with the management of other wildlife diseases within our shared ecosystems.