Deriving Spatial Waterfowl Inputs for Disease Risk Modeling Active
This project is an effort to create spatially and temporally explicit models for waterfowl distribution across the United States for use in avian influenza transmission risk modeling.
Our Research Into Avian Influenza Transmission
The Prosser Lab is working to understand all aspects of Avian Influenza Transmission both in the United States and across the globe!
Disease risk modeling can be an important tool for identifying areas of high transmission risk within and between animal populations, allowing for strategic allocation of limited resources for disease surveillance and prevention. Acquiring a spatial understanding of the distributions of high risk populations is a critical first step in developing predictive disease transmission models. One such disease is highly pathogenic avian influenza, outbreaks of which have caused concern for both domestic and wild populations in the United States.
We aim to use multiple spatial modeling approaches that incorporate a temporal component. A bayesian hierarchical approach using Integrated Nested Laplace Approximation (INLA) is being applied to develop dynamic spatial distribution models for waterfowl species of importance to highly pathogenic avian influenza (HPAI). This method allows for building multiple distribution models across the breeding, wintering, and migratory seasons, leading to more temporally detailed disease risk models. For example, instead of a single risk map showing areas of high transmission potential between wild and domestic birds, we aim for monthly or seasonal maps showing differences in areas of transmission potential at this interface.
The temporally dynamic spatial distribution models for waterfowl (10 dabbling duck species for this project) developed from this modeling will serve as inputs to avian influenza disease transmission models. Collaborations with the United States Department of Agriculture are ongoing to investigate transmission risk at the interface of poultry and wild waterfowl.
Key team members:
Dr. John M. Humphreys, USGS-UMD post-doc, University of Michigan
Dr. Jennifer L. Mullinax (Murrow), University of Maryland
Below are publications associated with this project.
Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle
Seasonal occurrence and abundance of dabbling ducks across the continental United States: Joint spatio-temporal modelling for the Genus Anas
Below are data or web applications associated with this project.
Story Map: Avian Influenza in the United States
This story map explores the role USGS scientists play in understanding the risks presented by avian influenza, and how their work is being used to predict areas of transmission risk.
Visualizing Models for Avian Influenza Viruses
Emergence of avian influenza viruses with the potential to be highly pathogenic to poultry, wild birds, & humans, such as the highly pathogenic H5N1 and H7N9 cause serious concern for the global economic & public health sectors. Visual representations of model data can be effective in helping to discover how the spread of the virus is influenced by environmental & human
Below are partners associated with this project.
- Overview
This project is an effort to create spatially and temporally explicit models for waterfowl distribution across the United States for use in avian influenza transmission risk modeling.
Our Research Into Avian Influenza TransmissionThe Prosser Lab is working to understand all aspects of Avian Influenza Transmission both in the United States and across the globe!
Disease risk modeling can be an important tool for identifying areas of high transmission risk within and between animal populations, allowing for strategic allocation of limited resources for disease surveillance and prevention. Acquiring a spatial understanding of the distributions of high risk populations is a critical first step in developing predictive disease transmission models. One such disease is highly pathogenic avian influenza, outbreaks of which have caused concern for both domestic and wild populations in the United States.
We aim to use multiple spatial modeling approaches that incorporate a temporal component. A bayesian hierarchical approach using Integrated Nested Laplace Approximation (INLA) is being applied to develop dynamic spatial distribution models for waterfowl species of importance to highly pathogenic avian influenza (HPAI). This method allows for building multiple distribution models across the breeding, wintering, and migratory seasons, leading to more temporally detailed disease risk models. For example, instead of a single risk map showing areas of high transmission potential between wild and domestic birds, we aim for monthly or seasonal maps showing differences in areas of transmission potential at this interface.
The temporally dynamic spatial distribution models for waterfowl (10 dabbling duck species for this project) developed from this modeling will serve as inputs to avian influenza disease transmission models. Collaborations with the United States Department of Agriculture are ongoing to investigate transmission risk at the interface of poultry and wild waterfowl.
Key team members:
Dr. John M. Humphreys, USGS-UMD post-doc, University of Michigan
Dr. Jennifer L. Mullinax (Murrow), University of Maryland - Publications
Below are publications associated with this project.
Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle
Avian influenza viruses can pose serious risks to agricultural production, human health, and wildlife. An understanding of viruses in wild reservoir species across time and space is important to informing surveillance programs, risk models, and potential population impacts for vulnerable species. Although it is recognized that influenza A virus prevalence peaks in reservoir waterfowl in late summeAuthorsCody M. Kent, Andrew M. Ramey, Josh T. Ackerman, Justin Bahl, Sarah N. Bevins, Andrew S. Bowman, Walter Boyce, Carol Cardona, Michael L. Casazza, Troy D. Cline, Susan E. W. De La Cruz, Jeffrey S. Hall, Nichola J. Hill, Hon S. Ip, Scott Krauss, Jennifer M. Mullinax, Jacqueline M. Nolting, Magdalena Plancarte, Rebecca L. Poulson, Jonathan A. Runstadler, Richard D. Slemons, David E. Stallknecht, Jeffery D. Sullivan, John Y. Takekawa, Richard J. Webby, Robert G. Webster, Diann J. ProsserSeasonal occurrence and abundance of dabbling ducks across the continental United States: Joint spatio-temporal modelling for the Genus Anas
Estimating the distribution and abundance of wildlife is an essential task in species conservation, wildlife management and habitat prioritization. Although a host of methods and tools have been proposed to accomplish this undertaking, several challenges remain in accurately forecasting occurrence and abundance for highly mobile species. Exhibiting extensive geographic ranges with seasonally varyiAuthorsJohn M. Humphreys, Jennifer L. Murrow, Jeffery D. Sullivan, Diann Prosser - Web Tools
Below are data or web applications associated with this project.
Story Map: Avian Influenza in the United States
This story map explores the role USGS scientists play in understanding the risks presented by avian influenza, and how their work is being used to predict areas of transmission risk.
Visualizing Models for Avian Influenza Viruses
Emergence of avian influenza viruses with the potential to be highly pathogenic to poultry, wild birds, & humans, such as the highly pathogenic H5N1 and H7N9 cause serious concern for the global economic & public health sectors. Visual representations of model data can be effective in helping to discover how the spread of the virus is influenced by environmental & human
- Partners
Below are partners associated with this project.