Researchers at USGS and specifically at the National Wildlife Health Center are working on developing new statistical and mathematical techniques and packaging them within user-friendly tools.
Some examples of new tools in development are web applications to analyze and interpret complex data, assess risk of future or ongoing disease outbreaks, estimate the effects of disease on individuals, populations, and ecosystems, and evaluate potential management solutions.
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When mechanism matters: Bayesian forecasting using models of ecological diffusion
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The Bayesian group lasso for confounded spatial data
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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 describe the best-known information regarding the
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Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of pAuthorsKathryn P. Huyvaert, Robin E. Russell, Kelly A. Patyk, Meggan E. Craft, Paul C. Cross, M. Graeme Garner, Michael K. Martin, Pauline Nol, Daniel P. WalshApplying a Bayesian weighted surveillance approach to detect chronic wasting disease in white‐tailed deer
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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 spatial random effects can make computation challengiAuthorsTrevor J. Hefley, Mevin Hooten, Ephraim M. Hanks, Robin E. Russell, Daniel P. WalshA 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 describe the best-known information regarding theAuthorsRobin E. Russell, Rachel A. Katz, Katherine L. D. Richgels, Daniel P. Walsh, Evan H. Campbell Grant