Computational Epidemiology

This course delves into the intersection of epidemiology and computational methods, equipping students with the tools to conduct rigorous epidemiological studies from big clinical data repositories. Students will explore techniques from informatics, computer science, machine learning, and statistics to clean, analyze, and interpret data from electronic health records (EHRs) and other large-scale datasets. Through hands-on projects and case students, students will gain practical experience in applying epidemiologic study designs to uncover patterns, identify risk factors, model disease transmission dynamics, and evaluate interventions. This interdisciplinary approach prepares students to address real-world public health challenges by leveraging the power of data-driven insights. The course is broken up into modules, each of which covers an epidemiologic study design or principle. Modules will range from 1-3 classes, and will include (i) a lecture and (ii) accompanying lab work. Students are expected to read technical texts carefully, participate actively in lecture discussion, and develop hands-on skills in labs involving real-world biomedical and health datasets. Students will curate their own analytic datasets from Observational Health Data Sciences and Informatics (OHDSI)-formatted electronic health record (SynPUF) data.