The Session began with a series of presentations by representatives of four different surveillance tool systems currently available or under development. Two of the presentations provided more information on systems described briefly in last month’s Outdoor News Bulletin. After the presentations, a forum to discuss related issues and a brief survey gauging audience interests were completed.

The CWD Alliance and D. J. Case have developed online CWD information mapping tools to help agencies better manage and control CWD in their state or province. Once beta testing is complete, the tools will be available through the Alliance website. Among other things, the dashboards will display counties and units where CWD has been found in captive and/or wild cervids. They also show CWD-related hunting regulations (e.g., carcass transport, use of urine products) that agencies can use to compare strategies and hunters can use to ensure they are complying with applicable laws or regulations. The online platform displays existing data that agencies can easily update and correct any time. All the agency needs to do is fill out a short data entry form with the pertinent information and submit. State and provincial agencies will receive a quarterly reminder to update this information to keep it timely and accurate. Some data will only be shared among agencies, and publicly facing data will only be shared after agencies have updated and approved it. This platform collects key information from a wide variety of locations in several different formats and displays it in a single, user-friendly location. It is readily expandable to provide additional CWD information if/as agencies agree to supply the data, and it could also be expanded to other wildlife diseases that involve presence, policy, and regulations. These mapping tools are the first of their kind to be created solely from the preferences and priority needs of state and provincial wildlife management agencies. They give agencies complete control and autonomy over the data, and they are expandable as future needs arise.

Michigan State University (MSU) researchers have developed several models that (a) provide improved focus for CWD surveillance efforts at varying stages of CWD emergence, and (b) predict the impacts of various management strategies on the outcomes of CWD and deer populations. To assist in surveillance, a quantitative method is available that combines expert opinions about CWD risk factors (e.g., captive cervid premises, out-of-state hunting in CWD endemic areas, carcass disposal practices) and address associated uncertainty to better identify CWD at the pre-detection phase. As well, there are a series of agent-based models (ABMs) that address specific hypotheses regarding CWD control or deer management among simulated individual deer. This approach allows managers the opportunity to ‘test drive’ various interventions and management strategies including new regulations packages before they are implemented to quantify whether or not the proposed interventions may help or hinder CWD prevalence and spread (based on the virtual environment and deer population).

The Surveillance Optimization Project for Chronic Wasting Disease (SOP4CWD) is a multi-institution collaboration that uses mathematical modeling and data science to help state and provincial wildlife agencies use their resources efficiently and develop data-driven surveillance and management programs. Wildlife agencies across eastern North America have been invited to contribute CWD surveillance data and information about their CWD surveillance activities and needs. In turn, participating agencies gain access to tools and resources in development by project partners. A team of application and database developers built a regional CWD surveillance system to integrate with existing wildlife agency CWD surveillance programs. The system includes a data warehouse and suite of web applications that provide wildlife agencies with CWD surveillance data management, analysis, and reporting features. A user-friendly interface for agency personnel will allow managers and field staff to explore sampling strategies, track progress to sampling goals, and provide data summaries and reports in real time during the hunting season. The data contributed by wildlife agencies will enhance the analyses and information available regionally, increasing our collective knowledge of CWD and improving every agency’s planning capacity.

The USGS National Wildlife Health Center presented their work on weighted surveillance, which is an AFWA CWD Best Management Practice for detecting new CWD foci on the landscape leveraging historical information from CWD-affected regions on variation of CWD risk across demographic groups within a population. The impetus for its development was the need to optimize the use of finite resources while maximizing the likelihood of early detection of CWD. To make the design and evaluation of CWD weighted surveillance systems accessible to wildlife managers, we created a user-friendly web application. The application provides two main menus with which the user can interface. The first “Design” menu is focused on providing tools for managers to design their weighted surveillance system, and also allows tracking of progress toward surveillance goals in real-time. The second “Estimation” menu is for use when managers have completed their surveillance and would like to examine what the potential prevalence could be in their population that went undetected given their sampling effort and the characteristics of the samples they tested.

The Pennsylvania Game Commission and Wildlife Futures Program at the University of Pennsylvania, with the expertise of the Timmons group created a visualization dashboard that tracks CWD sampling and test results to provide current CWD surveillance information to hunters, wildlife managers, and other stakeholders. The Game Commission dashboard for wild cervids displays data across three tabs: Samples Over Time, Sample Prevalence, and Sample Statistics. The PDA dashboard for captive cervids also displays data across three tabs: CWD Positive Facilities, Facility Statistics, and Facilities by Year.

Data managers and modelers are busy developing tools to forecast CWD growth and spread better than more standard modeling approaches. These approaches are being refined to allow their use in a web application that will provide wide accessibility to the CWD management community.