Daniel Walsh is a Quantitative Ecologist at the National Wildlife Health Center. Since joining the USGS in 2011, his research has focused on developing and applying quantitative approaches to understanding and managing wildlife disease processes. He has conducted applied research on a wide array of diseases including bighorn sheep respiratory disease, chronic wasting disease, Newcastle Disease, and Avian Influenza. He also conducts capacity building in wildlife disease management for countries throughout the world in collaboration with the World Organisation for Animal Health (OIE).
- Ph. D. Fisheries and Wildlife, Michigan State University, 2007
- M. S. Statistics Michigan State University, 2007
- M. S. Fish and Wildlife Biology, Colorado State University, 2002
- B. S. Fisheries and Wildlife, Michigan State University, 1999
- 2011 – Present Quantitative Ecologist, U.S. Geological Survey, National Wildlife Health Center, Madison WI
- 2007 – 2011 Disease Researcher, Colorado Division of Wildlife
- 2003 – 2007 Research Assistant-Michigan State University
- 2000 – 2002 Research Assistant-Colorado State University
- Honorary Fellow, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison
- Affiliate Faculty, South Dakota State University
- Member of the Wildlife Disease Association
- Member of the Wildlife Society
- Member of the North Central Section of the Wildlife Society
- Member of Wisconsin Chapter of the Wildlife Society
Science and Products
Chronic wasting disease (CWD) is an emerging infectious disease that is fatal to free-ranging and captive animals in Cervidae, the deer family. CWD is one member of a family of diseases called transmissible spongiform encephalopathies (TSEs), and is thought to be caused by prions. CWD is the only TSE known to affect free-ranging wildlife.
Applying a Bayesian weighted surveillance approach to detect chronic wasting disease in white‐tailed deer
Surveillance is critical for early detection of emerging and re‐emerging infectious diseases. Weighted surveillance leverages heterogeneity in infection risk to increase sampling efficiency.Here, we apply a Bayesian approach to estimate weights for 16 surveillance classes of white‐tailed deer in Wisconsin, USA, relative to hunter‐harvested...Jennelle, Christopher S.; Walsh, Daniel P.; Samuel, Michael D.; Osnas, Erik; Rolley, Robert E.; Langenberg, Julia A.; Powers, Jenny G.; Monello, Ryan J.; Demarest, E. David; Gubler, Rolf; Heisey, Dennis M.
Factors influencing elk recruitment across ecotypes in the Western United States
Ungulates are key components in ecosystems and economically important for sport and subsistence harvest. Yet the relative importance of the effects of weather conditions, forage productivity, and carnivores on ungulates are not well understood. We examined changes in elk (Cervus canadensis) recruitment (indexed as age ratios) across 7 states and 3...Lukacs, Paul M.; Mitchell, Michael S.; Hebblewhite, Mark; Johnson, Bruce K.; Johnson, Heather E.; Kauffman, Matthew J.; Proffitt, Kelly M.; Zager, Peter; Brodie, Jedediah; Hersey, Kent R.; Holland, A. Andrew; Hurley, Mark; McCorquodale, Scott; Middleton, Arthur; Nordhagen, Matthew; Nowak, J. Joshua; Walsh, Daniel P.; White, P.J.
Chronic wasting disease—Status, science, and management support by the U.S. Geological Survey
The U.S. Geological Survey (USGS) investigates chronic wasting disease (CWD) at multiple science centers and cooperative research units across the Nation and supports the management of CWD through science-based strategies. CWD research conducted by USGS scientists has three strategies: (1) to understand the biology, ecology, and causes and...Carlson, Christina M.; Hopkins, M. Camille ; Nguyen, Natalie T.; Richards, Bryan J.; Walsh, Daniel P.; Walter, W. David
Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality
Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while...Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.
Semi-quantitative assessment of disease risks at the human, livestock, wildlife interface for the Republic of Korea using a nationwide survey of experts: A model for other countries
Wildlife-associated diseases and pathogens have increased in importance; however, management of a large number of diseases and diversity of hosts is prohibitively expensive. Thus, the determination of priority wildlife pathogens and risk factors for disease emergence is warranted. We used an online questionnaire survey to assess release and...Hwang, Jusun; Lee, Kyunglee; Walsh, Daniel P.; Kim, SangWha; Sleeman, Jonathan M.; Lee, Hang
A dynamic spatio-temporal model for spatial data
Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-temporal data generating process. In many applications, a generalized linear mixed model (GLMM) is used with a random effect to account for spatial dependence and to provide optimal spatial predictions. Location-specific covariates are often included as fixed...Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin; Walsh, Daniel P.
When mechanism matters: Bayesian forecasting using models of ecological diffusion
Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical...Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.
The Bayesian group lasso for confounded spatial data
Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the...Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.
A framework for modeling emerging diseases to inform management
The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that...Russell, Robin E.; Katz, Rachel A.; Richgels, Katherine L. D.; Walsh, Daniel P.; Grant, Evan H. Campbell
No evidence of infection or exposure to Highly Pathogenic Avian Influenzas in peridomestic wildlife on an affected poultry facility
We evaluated the potential transmission of avian influenza viruses (AIV) in wildlife species in three settings in association with an outbreak at a poultry facility: 1) small birds and small mammals on a poultry facility that was affected with highly pathogenic AIV (HPAIV) in April 2015; 2) small birds and small mammals on a nearby poultry...Grear, Daniel A.; Dusek, Robert J.; Walsh, Daniel P.; Hall, Jeffrey S.
Concordance in diagnostic testing for respiratory pathogens of bighorn sheep
Reliable diagnostic tests are essential for disease investigation and management. This is particularly true for diseases of free-ranging wildlife where sampling is logistically difficult precluding retesting. Clinical assays for wildlife diseases frequently vary among laboratories because of lack of appropriate standardized commercial kits....Walsh, Daniel P.; Cassirer, E. Frances; Bonds, Michael D.; Brown, Daniel R.; Edwards, William H.; Weiser, Glen C.; Drew, Mark L.; Briggs, Robert E. ; Fox, Karen A.; Miller, Michael W.; Shanthalingam, Sudarvili; Srikumaran, Subramaniam; Besser, Thomas E.
When can the cause of a population decline be determined?
Inferring the factors responsible for declines in abundance is a prerequisite to preventing the extinction of wild populations. Many of the policies and programmes intended to prevent extinctions operate on the assumption that the factors driving the decline of a population can be determined. Exogenous factors that cause declines in abundance can...Hefley, Trevor J.; Hooten, Mevin B.; Drake, John M.; Russell, Robin E.; Walsh, Daniel P.