Spatial Risk Factors
September 2014 - August 2018
- Wisconsin Science Center
Problem statement: Data associated with wildlife disease monitoring are often spatially referenced, but the spatial information is not always utilized in formal statistical models for prediction and forecasting. So What? Why this research matters: The ability to formally make inferences about wildlife health at large spatial and temporal scales is challenging and new statistical methods are needed to incorporate existing data sources to improve our understanding of wildlife disease dyanmics. Collaboration/Partners: This project is in collaboration with scientists at the U.S. Geological Survey and Wisconsin Department of Natural Resources. Research That Informs Decisions: Formal statistical models that account for the spatio-temporal dynamics of disease spread can lead to be a better understanding of mechanisms and forecasts that aid in the management of wildlife populations.
|Research Publications||Publication Date|
|Hefley, T.J., M.B. Hooten, E.M. Hanks, R.E. Russell, and D.P. Walsh. (2017). The Bayesian spatial group lasso. Journal of Agricultural, Biological, and Environmental Statistics, 22: 42-59.||February 2017|
|Hefley, T.J., M.B. Hooten, E.M. Hanks, R.E. Russell, and D.P. Walsh. (2017). Dynamic spatio-temporal models for spatial data. Spatial Statistics, 20: 206-220.||March 2017|
|Hefley, T.J., M.B. Hooten, R.E. Russell, D.P. Walsh, and J. Powell. (2017). When mechanism matters: Forecasting the spread of disease using ecological diffusion. Ecology Letters, 20: 640-650.||September 2017|