Operations Research for Nature

Early Detection of Emerging Diseases and Invasive Species over Large Landscape

Emerging diseases and invasive species pose growing threats to public and ecosystem health. By the time the first case is detected, the disease or species may have been spreading unnoticed for an extended period. We developed a resource allocation model to minimize this unnoticed spread. It applies to areas where disease or species are likely to emerge, but have not yet been found.  

The model allocates a given budget between prevention and surveillance across many geographical sites, adaptive to new information and scale to large landscapes. It can also support budget planning, computing the minimum budget required to achieve certain detection delay times.  

Applying the model to chronic wasting disease (CWD) in New York state shows that the optimal strategy can achieve significantly earlier detection. This is a joint work with the Cornell Wildlife Health Lab. 

J. Wang, B. J. Hanley, N. E. Thompson, Y. Gong, D. P. Walsh, Y. Huang, C. Gonzalez-Crespo, J. G. Booth, J. N. Caudell, L. A. Miller, and K. L. Schuler (2025), Strategic Planning of Surveillance and Prevention for Emerging Diseases and Invasive Species, Proceedings of the National Academy of Sciences (PNAS) in press  

This new model has been implemented in the SOP4CWD data warehouse and ready for use. Links to the software,   user manual,  code 

Media coverage:  Wildlife conservation gets down to business, Cornell Chronicle (September 2025)

Conservation of Threatened Species

The Hainan gibbon is the rarest primate on the planet, with only 42 individuals known as of 2024.  Working with conservation biologists at the Hainan Tropical Rainforest National Park, we use operations research approaches to optimize the conservation of this rare flagship species. 

Learn more about our work from the paper: 

J. Wang, X. Song,  R. Yousefi, Z. Jiang (2024)  Optimal Learning and Management of Threatened Species, Management Science, 71(6): 4757-4776.

Managing Human-Wildlife Interface

Can analytics help reconcile the conflict between human economic interests and wildlife conservation? 

Ship strike is the leading cause of whale mortality. A widely used mitigation strategy is the implementation of vessel speed reduction (VSR) zones, where ships are asked to slow down. However, VSR can impose costs on mariners and lead to unintended shipping responses. Based on satellite data of ships and whales, we optimize the design of VSR to achieve a win–win outcome: saving whale while reducing the cost to the shipping industry.

Smart Speed, Sustainable Seas: Optimizing Speed Reduction Zones for Whale Conservation (work in progress)

 Collaborators: Yu Gong (PhD Candidate), Jingwei Zhang (Cornell), Jessica Morten (NOAA), Rachel Rhodes (Benioff Ocean Science Lab)

Optimizing Seasonal Biodiversity Surveys

In this project, we study how to collect data in changing environments, where data quality and costs vary over time, to inform adaptive management—an approach that updates decisions in response to new information.

 Collaborators: Yangyang  Xie,  Jeffrey Hanson, Joseph Bennett