Chronic wasting disease (CWD) is a fatal disease of white-tailed deer and other cervid species. Since 1967 when the disease was first discovered in captive mule deer in Colorado, CWD has spread across North America. Preventing further spread of the disease and controlling the disease after introduction have proven extremely difficult.

Disease surveillance, including efforts to detect new introductions quickly and measure changes in disease prevalence in areas where it exists, is an essential component of the disease response plans enacted by state wildlife agencies across the country. But where should state wildlife agencies focus their efforts? How much sampling is enough to determine if an area is ‘free of disease’ or to determine if the prevalence is increasing or decreasing? What does an efficient and effective disease surveillance plan look like?

White-tailed deer on the landscape

The Surveillance Optimization Project for Chronic Wasting Disease (SOP4CWD) aims to address these types of questions and provide state and provincial wildlife agencies with the quantitative tools they need to do so. SOP4CWD collaborators are applying methods from mathematical modeling and data science to address the challenges of disease surveillance, merging analytical techniques including risk weighting, Bayesian modeling, and geospatial analysis with machine learning algorithms.

The project is led jointly by the Cornell Wildlife Health Lab at Cornell University and the Boone and Crockett Quantitative Wildlife Center at Michigan State University. USGS contributes to the project as a research partner. Initial funding was provided by the Michigan Department of Natural Resources and New York State Department of Environmental Conservation.

Participating States and Provinces

Data and input from participating states and provinces play a key role in SOP4CWD. As of April 2021, wildlife agencies from fifteen US states and one Canadian province have joined SOP4CWD. Eight additional wildlife agencies are either in the process of completing formal agreements or are considering doing so.

Wildlife agencies provide important information about their CWD surveillance programs, including workflows and objectives. By engaging and contributing to SOP4CWD, wildlife agencies ensure that the analyses and products are relevant and can be integrated into existing surveillance programs.

Wildlife agencies also contribute historic surveillance data, which are critical inputs to the mathematical models that provide the foundation for more efficient surveillance approaches. Other data provided by wildlife agencies include population demographics; historic population management activities and hunting data; CWD sampling and test data; specific CWD introduction risks; and current surveillance program activities.

States and Provinces Participating in SOP4CWD

A new toolbox for wildlife agencies

The products of this effort will be integrated into a novel CWD surveillance system that is already in development. The core of the system will be a new CWD data warehouse that integrates CWD surveillance and testing data across wildlife agencies. A suite of integrated CWD surveillance, modeling, and management applications will provide wildlife agencies with the tools they need to efficiently manage their surveillance effort and make data-driven decisions.

The screenshots below provide some examples of the capabilities of the applications under development.

The CWD Sample Optimization App allows wildlife agencies to develop sampling quotas based on agency-specific goals and limitations, such as budget, disease transmission risk, and regional needs.
Agencies can explore different sampling strategies to ensure that agency goals are met.
Agencies can compare proposed county-level sampling quotas to previous years to improve resource allocation.

Although this system is designed for CWD surveillance in cervids in eastern North America, the framework under development can serve as a template for other regional disease surveillance and management situations.

Where do I sign up?

State and provincial wildlife agencies from eastern North America are encouraged to join. The more partners we have, the stronger and better the outcomes of this effort will be. If you represent a wildlife agency that is not currently participating, please contact the Cornell Wildlife Health Lab ( for information on how to join.

Wildlife agencies are encouraged to sign a data use agreement (DUA), but is is not a requirement of the project. The purpose of the DUA is to establish clear guidelines for data access, security, and use. Wildlife agencies are not asked to share data that would be considered personally identifiable information (PII).

SOP4CWD Project: Data Use Agreement

Project Timeline


The success of the project is measured by the benefits it provides to the participating states. Understanding each state’s surveillance objectives and workflows is essential to shaping the surveillance model and developing the system. An initial in-person meeting between state wildlife agency representatives, project leads, modelers, and developers was held in East Lansing, Michigan in January 2020. At this meeting, state agencies shared information about their surveillance programs and discussed the mathematical modeling and data science tools that are used in the project.


Model developers derive linkages between extant modeling formulas and data science techniques to synthesize regional data into actionable surveillance recommendations. Model development includes the mathematical derivation, software development, validations, and efficiency assessments of the new analytical tools. The toolset will provide for stakeholder predictions of CWD risk and surveillance recommendations.


The regional CWD surveillance database will build on existing systems states are already using to provide participants the means to transfer sample data, explore alternative strategies, and select an optimal sampling scheme given the agency’s goals and limitations. The SOP4CWD system should provide a user-friendly interface for state biologists to explore model options, track progress toward sampling goals, and provide summary reports in near real-time. The technology will be transferable and accompanied by appropriate documentation. The data will be used to continue to refine the model in subsequent sampling seasons. The system will also provide features that assist the states in surveillance planning and data management.


The SOP4CWD system will include state-specific surveillance recommendations given regional and local trends discovered in the data. States will be provided access to this system, which should integrate into their workflow. Adaptation of the system to future needs, ideas, or desires of the participating states will require additional capital and support from wildlife agencies. Our final meeting in Michigan will address the deployment and ongoing maintenance funding of this system.