Disease Ecology and Modeling

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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.

Climate Change and Elk Herds

(Public domain.)

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.

CWD predictive modeling

This figure shows the measured and forecasted growth and spread of CWD in white-tailed deer in th core area where CWD occurs in Wisconsin, USA.(Credit: Dan Walsh, USGS NWHC. Public domain.)


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. 

CWD Weighted Surveillance

Weighted Surveillance for Detection of Chronic Wasting Disease Web Application. (Public domain.)

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

Two mule deer in a field of yellow and green

(Credit: Tom Koerner, Fish and Wildlife Service. Public domain.)

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.