What do business and conservation have in common?
As it turns out, a lot.
Scientists who study business operations have been successful in creating models that guide appropriate decision-making despite limited information, all while keeping costs low. Given that research in wildlife health often requires budget decisions despite limited or incomplete information, we partnered with business professionals to explore ways to improve research efficiency and better serve wildlife. Our primary goal is to save money by optimizing operations on a fundable charismatic species, and then funnel those savings into research objectives for less fundable wildlife species.
Led by Dr. Jue Wang, Associate Professor at the Smith School of Business, Queen's University, Ontario, Canada, this work meshes innovative science in operations management and analytics with the most pressing needs in wildlife health and conservation. Our new work, entitled 'Strategic Planning of Prevention and Surveillance for Emerging Diseases and Invasive Species,' depicts a novel model that can aid wildlife agencies in planning cost-effective disease surveillance across large jurisdictions, despite unavoidable limitations in current information. While this model originated from wildlife health, it is also directly applicable to planning for disease surveillance by agricultural and public health agencies, as well as for planning surveillance of invasive species across conservation areas.
Zoonotic diseases that arise in wildlife are especially important subjects of study in the goal to protect human infrastructure. Disease is most controllable when only a few hosts are infected, but that is exactly when surveillance is most challenging. The subtle beginning of an outbreak means data is scarce, and a thorough investigation is most costly. Our new model considers disease dynamics and logistical costs of surveillance to pinpoint the best surveillance strategy for minimizing unseen spread and damage up to the moment of first detection. Better yet, the model is valuable for any planning horizon, for any number of sites, and in any disease/host system of interest, provided the disease has not yet been detected.
Code to run the model within the CWD Data Warehouse can be found at the CWHL Git Hub:
Sample Allocation Model Code
The full publication can be found here:
Wang J, Hanley B, Thompson N, Gong Y, Walsh D, Gonzalez-Crespo C, Huang Y, Booth J, Caudell J, Miller L, Schuler K. 2025. Strategic Planning of Prevention and Surveillance for Emerging Diseases and Invasive Species. PNAS. DOI: https://doi.org/10.1073/pnas.2507202122
The model is immediately available to 22 state wildlife agencies in North America through the SOP4CWD collaboration (sop4cwd.org). The SOP4CWD project, led by the Cornell Wildlife Health Lab, provides a free, automated technological system that enables agencies to manage disease surveillance data and make data-driven decisions to inform conservation outcomes. This new model is autonomously integrated into the CWD Data Warehouse, enabling immediate surveillance savings for research on chronic wasting disease (CWD).