Date of Submission

Spring 2020

Academic Program

Biology; Global Public Health

Project Advisor 1

Felicia Keesing

Abstract/Artist's Statement

Lyme disease is the most common vector-borne disease in North America. Annually, about 30,000 cases of Lyme are reported to the Centers for Disease Control and Prevention (CDC), though the true number of cases is estimated to be around 300,000. Much research has been conducted with the aim of elucidating to what degree various environmental factors contribute to incidence. A smaller subset of the field focuses purely on the geospatial relationships of Lyme incidence statistics, without incorporating ecological factors as explanatory variables, but with the understanding that Lyme is a spatially dependent disease. The present study investigates the spatial autocorrelation of patterns of Lyme incidence through time. Using the join count statistic, I found that a number of temporal incidence patterns are significantly and strongly spatially autocorrelated when binary spatial weights are used. These results are compatible with existing research about the geographic distribution and predicted spread of Lyme. I also concluded that sentinel surveillance and other case reporting issues play a significant role in the geospatial analysis of Lyme incidence. With these caveats in mind, the results of this study nonetheless demonstrate that certain temporal patterns of Lyme disease are spatially autocorrelated and thus warrant public health recommendations. The primary recommendations include 1) a greater awareness among local health departments of the Lyme statistics of not only their neighbors, but also areas which are a few counties over, and 2) improved communication on behalf of the CDC as to which local health departments use sentinel surveillance.

Open Access Agreement

On-Campus only

Creative Commons License

Creative Commons License
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