Self-Guided Learning of Statistic Analysis Through Lyme Disease and Climate Change as a Case Study

This paper summarizes how we approached answering a STEM research question. With minimal experience in statistical analysis, data handling, and Python, we follow the scientific process in a self-guided learning journey. This approach is designed to be an educational module for high-school instructors and students, enabling the replication of the analysis to foster a deeper understanding of statistical techniques and involved tools and technologies. Ticks, parasitic arachnids, transmit several diseases, notably Lyme disease, a significant human health concern. Caused by Borrelia burgdorferi and Borrelia mayonii, Lyme disease is primarily spread by black-legged ticks (Ixodes scapularis). Over recent decades, Lyme disease cases have risen sharply, concurrent with increasing global and regional temperatures. Climate change, especially the accelerated warming in the U.S., has expanded suitable tick habitats, allowing them to thrive in previously inhospitable areas. This study examines the connection between rising temperatures from global warming and the increased incidence of Lyme disease. We hypothesize that higher temperatures directly contribute to tick population growth and, consequently, more Lyme disease cases. Analyzing regional temperature and Lyme disease data in the U.S. using statistical methods, we seek to identify patterns and correlations. Our results aim to illuminate the ecological effects of climate change and inform preventative strategies against the growing Lyme disease threat.