Future Lyme disease risk in the south-eastern United States based on projected land cover

  • Logan K. Stevens | LKS12@vt.edu Department of Geography, Virginia Tech, Blacksburg, Virginia, United States.
  • Korine N. Kolivras Department of Geography, Virginia Tech, Blacksburg, Virginia, United States.
  • Yili Hong Department of Statistics, Virginia Tech, Blacksburg, Virginia, United States.
  • Valerie A. Thomas Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, Virginia, United States.
  • James B. Campbell Department of Geography, Virginia Tech, Blacksburg, Virginia, United States.
  • Stephen P. Prisley National Council for Air and Stream Improvement, North Carolina, United States.

Abstract

Lyme disease is the most significant vector-borne disease in the United States, and its southward advance over several decades has been quantified. Previous research has examined the potential role of climate change on the disease’s expansion, but no studies have considered the role of future land cover upon its distribution. This research examines Lyme disease risk in the south-eastern U.S. based on projected land cover developed under four Intergovernmental Panel on Climate Change Scenarios: A1B, A2, B1, and B2. Land cover types and edge indices significantly associated with Lyme disease in Virginia were incorporated into a spatial Poisson regression model to quantify potential land cover suitability for Lyme disease in the south-eastern U.S. under each scenario. Our results indicate an intensification of potential land cover suitability for Lyme disease under the A scenarios and a decrease of potential land cover suitability under the B scenarios. The decrease under the B scenarios is a critical result, indicating that Lyme disease risk can be decreased by making different land cover choices. Additionally, health officials can focus efforts in projected high incidence areas.

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Published
2019-05-14
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Section
Original Articles
Keywords:
Lyme disease, Medical geography, GIS, Spatial Poisson regression, Land cover, USA
Statistics
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How to Cite
Stevens, L. K., Kolivras, K. N., Hong, Y., Thomas, V. A., Campbell, J. B., & Prisley, S. P. (2019). Future Lyme disease risk in the south-eastern United States based on projected land cover. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.751