Disease Ecology and Modeling Active
The USGS National Wildlife Health Center (NWHC) provides quantitative support and technical assistance to state and federal wildlife managers and partners to better understand or predict the impact of disease on wildlife populations.
The USGS is a world-leader in developing statistical tools and predictive models for wildlife diseases including those that impact humans and domestic animals. Human, agricultural, and wildlife health are interlinked; thus risk assessment, prediction, and management of wildlife diseases are important for our nation’s health and economy. However, examining disease dynamics, monitoring and evaluating wildlife health are difficult and expensive endeavors. These challenges often make wildlife health datasets “messy”, and extracting the signal from the noise in these datasets is often challenging or impossible using traditional methods. The NWHC has advanced skills in statistics and mathematics, and develops and applies novel quantitative methods to overcome the complexities of wildlife health data, and ultimately better understand the ecology and spread of wildlife diseases.
The projects conducted by the NWHC, as described below, represent a substantial leap forward in statistical modeling of wildlife diseases. Continued work will focus on further development of these and other novel techniques and application of these statistical innovations to current and future disease conditions on the landscape, especially using additional diseases as case studies. Ultimately, we believe these approaches will be used across a wide array of diseases to understand the critical drivers of disease and its spread, predict future disease states, and determine appropriate interventions for the protection and improvement of human, domestic animal and ecosystem health.
Spatial Analytical Techniques for Predictive Modeling of Emerging Diseases
To be able to predict the spread and impact of biological threats – both invasive species and emerging infectious diseases - we need to first understand how these threats spread across the landscape. Understanding the processes that allow for their growth and spread is critical to permit development of effective management actions for protecting the health of humans, animals and the environment, and to forecast the risk and spread of these threats to new populations and regions. However, developing models to understand disease processes is challenging owing to the scale and large uncertainty inherent in wildlife disease systems as well as the lack of suitable analytical tools. This is complicated by incomplete or passive surveillance efforts, which have known bias and could lead to false conclusions. To overcome these obstacles, the NWHC, in collaboration with the Wisconsin Department of Natural Resources, Colorado State University Co-op unit, and Kansas State University, developed a statistical framework that uses advanced statistics to predict the growth and spread of disease within wildlife populations while correcting for sampling biases and properly incorporating uncertainty. The NWHC applied this statistical framework to model the spatial and temporal changes in Chronic Wasting Disease (CWD) using surveillance data collected by the Wisconsin Department of Natural Resources. These efforts clearly demonstrated its usefulness in describing historic disease patterns as well as forecasting future growth and spread of CWD. Further development of these exciting modeling tools will occur in 2018 and 2019. In particular, future work will use these new analytical techniques to evaluate the effectiveness of historic disease management actions, rigorously assess the origin of disease outbreaks, and link individual animal dynamics to system-wide outcomes. Ultimately, we believe these approaches will be used across a wide array of diseases to understand the critical drivers of disease and its spread, predict future disease states, and determine appropriate interventions for the protection and improvement of human, domestic animal and ecosystem health.
Power to the People: Developing Accessible Tools for Statistical Modeling
There is a wealth of new analytical techniques being developed for analyzing ecological data, including investigations of fish and wildlife diseases. Unfortunately, these developments are often highly technical and require computer programming acumen, and often remain inaccessible to the average scientist as well as fish and wildlife managers. This inaccessibility prevents both scientists and managers from learning when and how to apply appropriate statistical tools, which slows the progress of science and the effectiveness of management programs. To address this problem, the NWHC in collaboration with the University of Montana have begun development of web applications that make sophisticated statistical modeling techniques easily accessible to the average scientist. Beginning in 2016, an application was developed that focused on providing an efficient means of gathering and quantifying expert opinions. Expert elicitation is an important scientific technique for understanding problems which are not well studied or are novel (e.g., new emerging diseases). This application provided the ability to quickly gather and quantify a large number of experts’ opinions for further sophisticated statistical analyses.
In 2017, the NWHC developed a new application for designing and conducting weighted surveillance programs for CWD, which is a type of surveillance that focuses on detecting new disease foci in a cost-efficient and statistically rigorous manner. Weighted surveillance is gaining in popularity among wildlife agencies trying to manage the spread of CWD with limited resources, but tools for its application are not readily available. Currently, additional applications are being developed for the modeling of population demography and disease processes. The fruits of these research efforts will be used by the scientific and wildlife management community at large. This research aligns with the USGS Ecosystems Mission Area goal to develop scientific and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources.
Check out the web application at: https://popr.cfc.umt.edu/CWD/
Wisconsin CWD Deer Population Study
Chronic wasting disease is a fatal disease affecting deer, elk and moose. These species are highly valued by society for conservation and hunting. Evidence is mounting that CWD causes declines in affected big game populations, however, no one has conducted a long-term study to demonstrate these declines while accounting for other influences such as predators and habitat conditions. The NWHC is working in collaboration with the Wisconsin Department of Natural Resources and the University of Wisconsin to investigate the long-term population-level impacts of CWD on white-tailed deer using an ecosystem-level approach. Through intensive field research and advanced statistical modeling, this project will measure the impacts of this disease on free-ranging deer populations using integrated population models. The data collected will help state game management agencies determine what aspects of the disease process could be targeted for effective disease control efforts, help evaluate potential management actions, and predict future disease intensity and impacts on white-tailed deer populations.
Quantitative Applications in Disease Ecology
The NWHC is 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. The results from this project are broadly applicable to a variety of wildlife diseases, but current focus is on development of new statistical methods to predict the likelihood of virus isolation from samples collected for avian influenza surveillance and predicting the spread of CWD in the Midwest. This study represents a cooperative ecosystems studies unit partnership with the University of Wisconsin Department of Statistics.
Partnerships in Emerging Wildlife Disease Epidemiology and Modeling
The NWHC routinely provides technical assistance to state, federal, and international wildlife managers who want to better understand or predict the impact of disease on wildlife populations using the advanced skills we have in statistics and mathematics. For example, the NWHC developed a model for CWD in Colorado that used several different datasets and incorporated sociological data as well as population level data to get better estimates of herd size and health. By providing necessary quantitative support to wildlife health problems, this technical assistance has directly and positively impacted wildlife health management across the nation.
Below are publications related to ecology and modeling.
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
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
The Bayesian group lasso for confounded spatial data
A framework for modeling emerging diseases to inform management
When can the cause of a population decline be determined?
Evaluation of Yersinia pestis transmission pathways for sylvatic plague in prairie dog populations in the western U.S.
Spatial variation in risk and consequence of Batrachochytrium salamandrivorans introduction in the USA
Below are news stories associated with this project.
- Overview
The USGS National Wildlife Health Center (NWHC) provides quantitative support and technical assistance to state and federal wildlife managers and partners to better understand or predict the impact of disease on wildlife populations.
The USGS is a world-leader in developing statistical tools and predictive models for wildlife diseases including those that impact humans and domestic animals. Human, agricultural, and wildlife health are interlinked; thus risk assessment, prediction, and management of wildlife diseases are important for our nation’s health and economy. However, examining disease dynamics, monitoring and evaluating wildlife health are difficult and expensive endeavors. These challenges often make wildlife health datasets “messy”, and extracting the signal from the noise in these datasets is often challenging or impossible using traditional methods. The NWHC has advanced skills in statistics and mathematics, and develops and applies novel quantitative methods to overcome the complexities of wildlife health data, and ultimately better understand the ecology and spread of wildlife diseases.
The projects conducted by the NWHC, as described below, represent a substantial leap forward in statistical modeling of wildlife diseases. Continued work will focus on further development of these and other novel techniques and application of these statistical innovations to current and future disease conditions on the landscape, especially using additional diseases as case studies. Ultimately, we believe these approaches will be used across a wide array of diseases to understand the critical drivers of disease and its spread, predict future disease states, and determine appropriate interventions for the protection and improvement of human, domestic animal and ecosystem health.
Spatial Analytical Techniques for Predictive Modeling of Emerging Diseases
To be able to predict the spread and impact of biological threats – both invasive species and emerging infectious diseases - we need to first understand how these threats spread across the landscape. Understanding the processes that allow for their growth and spread is critical to permit development of effective management actions for protecting the health of humans, animals and the environment, and to forecast the risk and spread of these threats to new populations and regions. However, developing models to understand disease processes is challenging owing to the scale and large uncertainty inherent in wildlife disease systems as well as the lack of suitable analytical tools. This is complicated by incomplete or passive surveillance efforts, which have known bias and could lead to false conclusions. To overcome these obstacles, the NWHC, in collaboration with the Wisconsin Department of Natural Resources, Colorado State University Co-op unit, and Kansas State University, developed a statistical framework that uses advanced statistics to predict the growth and spread of disease within wildlife populations while correcting for sampling biases and properly incorporating uncertainty. The NWHC applied this statistical framework to model the spatial and temporal changes in Chronic Wasting Disease (CWD) using surveillance data collected by the Wisconsin Department of Natural Resources. These efforts clearly demonstrated its usefulness in describing historic disease patterns as well as forecasting future growth and spread of CWD. Further development of these exciting modeling tools will occur in 2018 and 2019. In particular, future work will use these new analytical techniques to evaluate the effectiveness of historic disease management actions, rigorously assess the origin of disease outbreaks, and link individual animal dynamics to system-wide outcomes. Ultimately, we believe these approaches will be used across a wide array of diseases to understand the critical drivers of disease and its spread, predict future disease states, and determine appropriate interventions for the protection and improvement of human, domestic animal and ecosystem health.
Power to the People: Developing Accessible Tools for Statistical Modeling
There is a wealth of new analytical techniques being developed for analyzing ecological data, including investigations of fish and wildlife diseases. Unfortunately, these developments are often highly technical and require computer programming acumen, and often remain inaccessible to the average scientist as well as fish and wildlife managers. This inaccessibility prevents both scientists and managers from learning when and how to apply appropriate statistical tools, which slows the progress of science and the effectiveness of management programs. To address this problem, the NWHC in collaboration with the University of Montana have begun development of web applications that make sophisticated statistical modeling techniques easily accessible to the average scientist. Beginning in 2016, an application was developed that focused on providing an efficient means of gathering and quantifying expert opinions. Expert elicitation is an important scientific technique for understanding problems which are not well studied or are novel (e.g., new emerging diseases). This application provided the ability to quickly gather and quantify a large number of experts’ opinions for further sophisticated statistical analyses.
In 2017, the NWHC developed a new application for designing and conducting weighted surveillance programs for CWD, which is a type of surveillance that focuses on detecting new disease foci in a cost-efficient and statistically rigorous manner. Weighted surveillance is gaining in popularity among wildlife agencies trying to manage the spread of CWD with limited resources, but tools for its application are not readily available. Currently, additional applications are being developed for the modeling of population demography and disease processes. The fruits of these research efforts will be used by the scientific and wildlife management community at large. This research aligns with the USGS Ecosystems Mission Area goal to develop scientific and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources.
Check out the web application at: https://popr.cfc.umt.edu/CWD/
Wisconsin CWD Deer Population Study
Chronic wasting disease is a fatal disease affecting deer, elk and moose. These species are highly valued by society for conservation and hunting. Evidence is mounting that CWD causes declines in affected big game populations, however, no one has conducted a long-term study to demonstrate these declines while accounting for other influences such as predators and habitat conditions. The NWHC is working in collaboration with the Wisconsin Department of Natural Resources and the University of Wisconsin to investigate the long-term population-level impacts of CWD on white-tailed deer using an ecosystem-level approach. Through intensive field research and advanced statistical modeling, this project will measure the impacts of this disease on free-ranging deer populations using integrated population models. The data collected will help state game management agencies determine what aspects of the disease process could be targeted for effective disease control efforts, help evaluate potential management actions, and predict future disease intensity and impacts on white-tailed deer populations.
Quantitative Applications in Disease Ecology
The NWHC is 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. The results from this project are broadly applicable to a variety of wildlife diseases, but current focus is on development of new statistical methods to predict the likelihood of virus isolation from samples collected for avian influenza surveillance and predicting the spread of CWD in the Midwest. This study represents a cooperative ecosystems studies unit partnership with the University of Wisconsin Department of Statistics.
Partnerships in Emerging Wildlife Disease Epidemiology and Modeling
The NWHC routinely provides technical assistance to state, federal, and international wildlife managers who want to better understand or predict the impact of disease on wildlife populations using the advanced skills we have in statistics and mathematics. For example, the NWHC developed a model for CWD in Colorado that used several different datasets and incorporated sociological data as well as population level data to get better estimates of herd size and health. By providing necessary quantitative support to wildlife health problems, this technical assistance has directly and positively impacted wildlife health management across the nation.
- Publications
Below are publications related to ecology and modeling.
Filter Total Items: 29Challenges and opportunities developing mathematical models of shared pathogens of domestic and wild animals
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
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 yearling males. We used these weights to conduct a surveilAuthorsChristopher S. Jennelle, Daniel P. Walsh, Michael D. Samuel, Erik E. Osnas, Robert E. Rolley, Julia A. Langenberg, Jenny G. Powers, Ryan J. Monello, E. David Demarest, Rolf Gubler, Dennis M. HeiseyChronic 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 distribution of CWD; (2) to assess and predict the spreadAuthorsChristina M. Carlson, M. Camille Hopkins, Natalie T. Nguyen, Bryan J. Richards, Daniel P. Walsh, W. David WalterUsing 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 properly accounting for associated uncertainty during analAuthorsDaniel P. Walsh, Andrew S. Norton, Daniel J. Storm, Timothy R. Van Deelen, Dennis M. HeisySemi-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 exposure risks, and consequences of wildlife-associated dAuthorsJusun Hwang, Kyunglee Lee, Daniel P. Walsh, SangWha Kim, Jonathan M. Sleeman, Hang LeeA 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 effects in a GLMM and may be collinear with the spatAuthorsTrevor J. Hefley, Mevin Hooten, Ephraim M. Hanks, Robin Russell, Daniel P. WalshWhen 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 Bayesian models of ecological diffusion can be impleAuthorsTrevor J. Hefley, Mevin Hooten, Robin E. Russell, Daniel P. Walsh, James A. PowellThe 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 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 GrantWhen 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 be statistically confounded with endogenous factorsAuthorsTrevor J. Hefley, Mevin Hooten, John M. Drake, Robin E. Russell, Daniel P. WalshEvaluation of Yersinia pestis transmission pathways for sylvatic plague in prairie dog populations in the western U.S.
Sylvatic plague, caused by the bacterium Yersinia pestis, is periodically responsible for large die-offs in rodent populations that can spillover and cause human mortalities. In the western US, prairie dog populations experience nearly 100% mortality during plague outbreaks, suggesting that multiple transmission pathways combine to amplify plague dynamics. Several alternate pathways in addition toAuthorsKatherine L. D. Richgels, Robin E. Russell, Gebbiena Bron, Tonie E. RockeSpatial variation in risk and consequence of Batrachochytrium salamandrivorans introduction in the USA
A newly identified fungal pathogen, Batrachochytrium salamandrivorans (Bsal), is responsible for mass mortality events and severe population declines in European salamanders. The eastern USA has the highest diversity of salamanders in the world and the introduction of this pathogen is likely to be devastating. Although data are inevitably limited for new pathogens, disease-risk assessments use besAuthorsKatherine L. D. Richgels, Robin E. Russell, M. J. Adams, C. LeAnn White, Evan H. Campbell Grant - News
Below are news stories associated with this project.