ABS 110: Modeling the Effects of Climate Change on Lyme Disease

Carolyne Nguyen ¹

¹ Brown University School of Public Health, Brown University

Van Wickle (2025) Volume 1, ABS 110

Abstract: Lyme disease is a vector-borne disease caused by the bacterium B. burgdorferi, which is transmitted to hosts (i.e., humans, deer, etc.) via vectors (black-legged ticks). In recent decades, Lyme disease incidence has surged with disproportionate rates in the United States Northeast region. Prior studies have correlated this increase to rising temperatures, precipitation, and ecological disturbances. Climate change poses significant challenges particularly for vector-borne diseases, whose transmission dynamics are heavily influenced by environmental conditions. Rising temperatures accelerate the life cycle of black-legged ticks, prolonging and intensifying future tick seasons. Nonetheless, there is a lack of application in rigorous infectious disease modeling to predict future outbreaks and inform public health responses. This study integrates the effects of climate change on Lyme disease transmission by presenting a comprehensive approach combining empirical data analysis with theoretical modeling. Analyzing state-level Lyme disease case data from the United States over a 20-year period, we first diagnose certain phenomena such as seasonality and geographic isolation. We focus our analysis on a modified Susceptible-Infected-Recovered (SIR) model that explicitly incorporates vector populations and temperature-dependent transmission rates. While previous studies have examined either empirical trends or theoretical models in isolation, our work bridges this gap by using robust data analysis to leverage our mechanistic modeling. This integrated approach enables us to both validate observed patterns and explore future scenarios under different climate projections. Our simulations and analyses confirm existing hypotheses about how temperature fluctuation affects Lyme disease transmission, in which we demonstrate that small fluctuations in temperature result in substantially larger outbreak peaks.Through further simulation studies, we interestingly reveal that increasing temperature around winter months has the largest effect on peak infection rate and speed. These findings suggest an urgent need for adaptive public health strategies to address the growing threat of Lyme disease under continued climate change