Disease Decision Analysis and Research
The Disease Decision Analysis and Research group is a multi-disciplinary team based out of the Eastern Ecological Science Center whose strengths are in ecology, decision sciences and quantitative modeling.
For almost two decades, the Eastern Ecological Science Center (EESC) has been an international leader in wildlife disease ecology, quantitative ecology, and decision science. EESC has played an instrumental role in understanding and planning management responses to disease systems including white-nose syndrome, Lyme disease, avian influenza, Batrachochytrium dendrobatidis, Batrachochytrium salamandrivorans (Bsal), and other emerging and zoonotic diseases. At the same time, EESC have used the principles of decision science to aid decision makers in the Department of Interior as well as other federal and state agencies, in topics as diverse as endangered species recovery and large river management. In recent years, EESC scientists have turned attention to the application of decision science to disease management problems, with the ability to integrate field and experimental studies, advanced statistical analyses, spatial epidemiological modeling, and decision analysis.
EESC has worked with management agency partners at Centers for Disease Control and Prevention, U.S. National Park Service, U.S. Department of Agriculture, U.S. Fish and Wildlife Service, U.S. Forest Service, Association of Fish and Wildlife Agencies, and other agencies to provide actionable science related to wildlife and human disease management. In recognition of the emerging capabilities at the interface of science and management, and in response to an increasing number of partner agency requests for help in developing and evaluating options for wildlife disease management, EESC established the Disease Decision Analysis and Research group in 2021 as an inter-disciplinary and cross-center project develop tools and analysis that aid decision makers in managing diseases with a wildlife nexus.
Acting under high levels of uncertainty is a hallmark of wildlife disease management, and the use of formal decision analytics (e.g., multi-criteria decision analysis, risk analysis, and cost-benefit analysis within a structured or adaptive management framework) is useful and increasingly necessary as a rational and transparent framework to support management of diseases and mitigation of disease risk. Decision analytic approaches can examine trade-offs between managing in the face of uncertainty and waiting to act in order to gain additional disease information, as well as identify key trade-offs among objectives which are often highly influential in optimizing disease responses.
The increasing number of novel infectious diseases of wildlife, and the human nexus in pathogen transmission, outbreaks, and zoonotics has resulted in substantial population declines in fish and wildlife species and biodiversity losses. Uncertainty in the ecological system is often a principal challenge and is a focus of research for Evan Grant, Diann Prosser, and Howard Ginsberg. Other major challenges include limited control options for the initial introduction of disease; widely dispersed populations over multiple states and regions; fragmented management authority and responsibility by diverse agencies (e.g. tribal, state, federal, and non-profits); and deep uncertainties in ecological characteristics of the pathogens, host populations, and the effectiveness of potential treatments.
Although there are substantial variations in pathogen, host, and environmental characteristics among invasive pathogens, we find that there are similar challenges to disease management decisions. Our team’s decision analysts Evan Grant, Jonathan Cook and Michael Runge help managers to recognize and address all possible impediments to effective responses –including elements of complexity in the social and the ecological systems, conflict in missions and mandates of resources that overlap multiple jurisdictions, a need for predictions to help identify the best option despite the multiple layers of complexity, and a need to improve creativity as there are often few tools available to respond to disease.
Explore our disease decision analysis and research: