Daniel P. Walsh, PhD
Assistant Unit Leader - Montana Cooperative Wildlife Research Unit
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).
Professional Experience
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
Education and Certifications
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
Affiliations and Memberships*
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
Removal of chronic Mycoplasma ovipneumoniae carrier ewes eliminates pneumonia in a bighorn sheep population
Chronic wasting disease—Research by the U.S. Geological Survey and partners
Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses
Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl
Challenges and opportunities developing mathematical models of shared pathogens of domestic and wild animals
Applying a Bayesian weighted surveillance approach to detect chronic wasting disease in white‐tailed deer
Factors influencing elk recruitment across ecotypes in the Western United States
Chronic wasting disease—Status, science, and management support by the U.S. Geological Survey
Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality
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
A dynamic spatio-temporal model for spatial data
When mechanism matters: Bayesian forecasting using models of ecological diffusion
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Removal of chronic Mycoplasma ovipneumoniae carrier ewes eliminates pneumonia in a bighorn sheep population
Chronic wasting disease—Research by the U.S. Geological Survey and partners
Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses
Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl
Challenges and opportunities developing mathematical models of shared pathogens of domestic and wild animals
Applying a Bayesian weighted surveillance approach to detect chronic wasting disease in white‐tailed deer
Factors influencing elk recruitment across ecotypes in the Western United States
Chronic wasting disease—Status, science, and management support by the U.S. Geological Survey
Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality
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
A dynamic spatio-temporal model for spatial data
When mechanism matters: Bayesian forecasting using models of ecological diffusion
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government