Sarah and a citiscape blending into R interface

Student research: Sarah Weiner

In recent years, many have speculated that climate change is the driving force behind the spike in cases of Lyme disease in the northeastern United States.  It would be really useful for the public if we could use climate data to predict places and times at risk for Lyme disease.  In her senior project, Sarah Weiner used public records from the United States Drought Monitor to create a climate index.  Then, with guidance from professor Felicia Keesing, Sarah built statistical models to see whether this climate index could be used to predict year-to-year variation in Lyme disease incidence at the county level.  Sarah found that climate is not a practical way to predict Lyme disease outbreaks, and that other factors, such as location, are much better predictors.