Date of Submission

Spring 2018

Academic Programs and Concentrations

Biology; Global Public Health

Project Advisor 1

Felicia Keesing

Abstract/Artist's Statement

Lyme disease (Lyme borreliosis) is a tick-borne illness spread through the bite of a blacklegged tick (Ixodes scapularis) that is infected with the bacterial spirochete Borrelia burgdorferi. In recent years, there has been a notable increase in Lyme disease incidence in the northeastern United States. Ample research shows that climate can affect the survival and behavior of ticks, which could alter their patterns of contact with humans, potentially influencing the incidence of Lyme disease. Developing a reliable, easily accessible climate index to predict places and times of heightened risk for Lyme disease would be valuable for public health initiatives. In this study, I used the United States Drought Monitor to develop an interannual drought index for the questing periods of nymphal and larval blacklegged ticks. I asked whether this index could be used to predict incidence of Lyme disease in emerging and endemic regions of the northeastern United States at the county level. I compared the strength of generalized additive mixed models built using the drought index to models developed by Burtis et al. (2016) that used the number of hot, dry days (defined as days when average maximum temperature was greater than 25°C and there was no measurable precipitation) to predict interannual variation in Lyme disease incidence. Although my models were similar in strength to those of Burtis et al. (2016), the difference in real explanatory ability of any of the models that included climate variables was negligible compared to a base model that did not include weather variables. These results suggest that climate is not a practical or particularly effective way to predict interannual variation in Lyme disease incidence, and that other factors, such as location, are better predictors.

Open Access Agreement

On-Campus only

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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