Skip to page navigation menu Skip entire header
Brown University
Skip 13 subheader links

Predicting Lyme Disease Cases in the Northeastern United States Using Remote Sensing and Weather Station Data

Description

Abstract:
Lyme disease affects hundreds of thousands of Americans each year. While treatable if caught in time, the disease is associated with massive annual healthcare costs. If public health professionals and other researchers are able to determine which geographical regions are likely to see the largest numbers of Lyme disease cases during peak Lyme disease season, they can better target their preventative and monitoring resources, and ideally reduce the number of infections and associated medical costs. When coupled with a priori knowledge of tick behavior and Lyme disease transmission, remote sensing and climate data can provide information to assist in these geographical predictions. Lyme disease cases per thousand by county and year in 13 northeastern U.S. states for each year from 2000-2018 (n=4681) were compared with average temperature and average precipitation during February through April as well as average leaf area index on April 1 for the same counties in the months leading up to the heaviest part of the related Lyme disease season. Precipitation and temperature were shown to be significantly and positively associated with cases per thousand of Lyme disease, and leaf area index was shown to be significantly and negatively associated with cases per thousand of Lyme disease. The model, however, did not account for much of the variance in disease incidence, suggesting there are many other unmeasured variables that should be explored. Overall, the results of this analysis suggest that remote sensing and climate data sources are helpful in predicting regions that will see fluctuations in Lyme disease incidence in future months, and that remote sensing data should be further explored when attempting predict many other health outcomes.
Notes:
Thesis (M. P. H.)--Brown University, 2021

Citation

Hester, Jack, "Predicting Lyme Disease Cases in the Northeastern United States Using Remote Sensing and Weather Station Data" (2021). Public Health Theses and Dissertations. Brown Digital Repository. Brown University Library. https://repository.library.brown.edu/studio/item/bdr:g3snykd7/

Relations

Collection: