Spatial epidemiology

Health and disease are not randomly distributed in space. Where people live, work, and move shapes their exposures, risks, and health outcomes. This course introduces students to the principles and methods of spatial epidemiology, with a focus on understanding and analyzing geographic patterns in public health data. The course combines conceptual foundations with hands-on analytical skills. Students will learn how to work with spatial data, visualize geographic variation in health and its determinants, and identify patterns such as clustering and spatial dependence. Building on these foundations, the course introduces key analytical approaches used in modern research, including exploratory spatial data analysis, spatial regression models, and an introduction to Bayesian disease mapping. A central theme of the course is linking methods to real-world public health questions. Through applied examples—such as health inequalities, access to healthcare, and neighborhood-level determinants of health—students will learn how spatial methods can generate insights that are directly relevant for research and policy. The course also emphasizes critical thinking. Students will reflect on important challenges in spatial epidemiology, including issues of data quality, spatial scale, and interpretation of area-level analyses. By the end of the course, students will be equipped not only to conduct spatial analyses, but also to critically evaluate spatial evidence in epidemiological research.