Epidemiological Modeling

Epidemiological models can start with a basic description of disease data, but as we build complex models based on solid data, we can achieve informative estimates of several important parameters driving disease processes such as infection, spread, and population impacts.

 

Observed female (triangle) and male (circle) prevalence (with 95% CI) based on harvest data from south-central WI from 2002-2011.  Lines show model predictions of prevalence for males and females using a sex-specific frequency-dependent model.

Observed female (triangle) and male (circle) prevalence (with 95% CI) based on harvest data from south-central WI from 2002-2011. Lines show model predictions of prevalence for males and females using a sex-specific frequency-dependent model.

 

Computer-based modeling is often used in wildlife epidemiology to assist in management of
wildlife disease outbreaks. These mathematical representations of complex epidemiological
systems are helpful in determining the role of individual risk factors and the complicated
interactions among factors affecting disease dynamics. Models allows researchers to integrate knowledge from a variety of studies to integrate established and new research results and examine alternative strategies for disease management through computer simulation. Epidemiological modeling has been used to provide insight into a variety of disease problems, including foot and mouth disease in the UK, rabies in Europe and North America, bovine tuberculosis in the United Kingdom, New Zealand, and Canada, bovine brucellosis in North America, and chronic wasting disease in the western United States.

Our lab works closely with wildlife management agencies and collaborating researchers
to design epidemiological models that, not only improve our theoretical understanding of
disease systems, but also provide practical information that can be applied to wildlife disease management. Our primary modeling objectives include:

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  •  Determine sensitivity of disease dynamics to transmission model (spatial and demographic spread, prevalence, spatial patterns), and other epidemiological factors such as disease-induced mortality.

 

 

 

 

 

 

 

 

 

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