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Applied geography (Sevenoaks, England)2014; 48; 79-86; doi: 10.1016/j.apgeog.2014.01.012

A risk index model for predicting eastern equine encephalitis virus transmission to horses in Florida.

Abstract: A GIS-based risk index model was developed to quantify EEEV transmission risk to horses in the State of Florida. EEEV is a highly pathogenic arbovirus that is endemic along the east coast of the United States, and it is generally fatal to both horses and humans. The model evaluates EEEV transmission risk at individual raster cells in map on a continuous scale of 0 to 1. The risk index is derived based on local habitat features and the composition and configuration of surrounding land cover types associated with EEEV transmission. The model was verified and validated using the locations of documented horse cases of EEEV. These results of the verification and validation indicate that the model is able to predict locations of EEEV transmission to horses broadly across the state. The model is relatively robust to regional variation in EEEV transmission and habitat conditions in Florida, and it accurately predicted nearly all verification and validation cases in the Panhandle, North, and Central regions of the state. The model performed less accurately in the South, where relatively few cases are documented. Despite these differences, the model provides a useful way to assess EEEV risk both from a regional perspective and at more localized scales. The resulting predictive maps are designed to guide EEEV surveillance and prevention efforts by county mosquito control districts.
Publication Date: 2014-04-26 PubMed ID: 24764607PubMed Central: PMC3993996DOI: 10.1016/j.apgeog.2014.01.012Google Scholar: Lookup
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  • Journal Article

Summary

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The research article discusses the creation of a GIS-based model that is able to predict areas where Eastern Equine Encephalitis Virus (EEEV) could be transmitted to horses in Florida.

Objective

This study aimed to create an accurate predictive model that quantifies EEEV transmission risk to horses in Florida. The model can aid in the surveillance of EEEV and guide prevention efforts.

Methods

  • The team developed a risk index model using GIS (Geographic Information System), a computer system used for capturing, storing, checking, and displaying data related to positions on Earth’s surface.
  • The model evaluates the risk of EEEV transmission at individual raster cells, a type of pixel with a value. The risk index is graded on a continuous scale, from 0 to 1, and is calculated based on local habitat features and the surrounding land cover types associated with EEEV transmission.
  • To validate the model, researchers cross-referenced the approximated risk areas with known cases of EEEV transmission in horses.

Results

  • The researchers found that the model effectively predicted the areas of EEEV transmission broadly across Florida. It was especially efficient in predicting cases in the Panhandle, North, and Central regions of the state.
  • The South region of Florida had fewer cases; hence the model was less accurate in this area.
  • Regardless of regional differences, the model was deemed useful in quantifying EEEV risk at both a regional and more localized scale.
  • The model’s results were subsequently used to create predictive maps, which are aimed to assist with guiding surveillance and prevention of EEEV by county mosquito control districts.

Conclusion

Through this study, the team successfully created a risk index model that can predict EEEV transmission risk to horses in Florida. Despite regional differences, the model proved to be a valuable tool for assessing EEEV risk. The predictive maps produced as a result of the model can now be used to guide EEEV surveillance and prevention efforts.

Cite This Article

APA
Kelen PV, Downs JA, Unnasch T, Stark L. (2014). A risk index model for predicting eastern equine encephalitis virus transmission to horses in Florida. Appl Geogr, 48, 79-86. https://doi.org/10.1016/j.apgeog.2014.01.012

Publication

ISSN: 0143-6228
NlmUniqueID: 101085119
Country: England
Language: English
Volume: 48
Pages: 79-86

Researcher Affiliations

Kelen, Patrick Vander
  • Department of Global Health, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620.
Downs, Joni A
  • School of Geosciences, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620.
Unnasch, Thomas
  • Department of Global Health, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620.
Stark, Lillian
  • Department of Global Health, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620.

Grant Funding

  • R01 AI049724 / NIAID NIH HHS
  • R56 AI101072 / NIAID NIH HHS

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Citations

This article has been cited 5 times.
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    doi: 10.4269/ajtmh.18-0783pubmed: 30860014google scholar: lookup
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