Abstract
Focusing on Northeastern United States, this project delves into the spatial distribution of Lyme disease across fourteen states. Lyme disease is the most common vector borne disease in the United States, and an endemic in Asia and Europe. In our area of focus, there were 38,069 reported cases of Lyme disease in 2015 alone (CDC, 2017; Adams et al., 2017). From previous years, it was observed that the geographical range of Lyme disease was increasing, as well as the number of reported cases. Spread by bacteria-carrying ticks, Lyme disease acts as a detriment to health for those infected. Hence, the alarming rise in the disease has prompted this study.
By investigating the ecological needs of ticks, this study uses MaxEnt technology to create suitability predictions of tick habitats, and has produced four models that use ecological variables to infer tick existence and therefore Lyme disease. Model 1 uses purely climatic variables such as temperature, vapour pressure, and precipitation. Model 2 uses climatic variables coupled with vegetation variables, Model 3 uses climatic and vegetation variables, along with deer population data, which act as host variables, and Model 4 presents an extrapolation of climatic variables into the future. It was found that all Models produced a prediction accuracy of 74%, with the exception of Model 1, which produced an accuracy of 68%. Spanning across all the Models, the variables of mean temperature of the coldest quater, ecoregions, isothermal, annual mean temperature served as the best predictions of tick habitat suitability, when used both individually and in conjunction with other variables. Using purely the climatic variables, Model 4 predicted a spatial increase in tick habitats, and particularly a northward spread. Hence, it is clear that tick habitats, and therefore the chances of Lyme disease contraction, are increasing, with a high possibility of the disease moving into Canada.
Next section: Introduction and Background