I teach two formal courses at Emory University focusing on infectious disease epidemiology, and I also teach short courses outside Emory that primarily focus on the EpiModel modeling platform.
EPI 570. Infectious Disease Dynamics: Theory and Methods. Spring Semester, 3 credits.
This course the theory, mathematical framework, and computational methods for investigating the mechanics of infectious disease dynamics. Students learn why these models are used in infectious disease epidemiology, compared to other quantitative methods; how to read and evaluate the epidemiological modeling literature across many disease areas; and hands-on skills to develop computational models for epidemics. Class hours are split between lecture, discussion, and computer labs each week.
EPI 512. Current Topics in Infectious Disease Epidemiology. Fall Semester, 1 credit.
This course is a requirement of the Infectious Disease Epidemiology Certificate at RSPH. It provides incoming students an orientation to the breadth of current topics in the field of infectious disease epidemiology, the certificate requirements, opportunities for research and extracurricular learning opportunities during the MPH/MSPH degree, and academic and professional opportunities after graduation. We introduce current "hot topics"" in infectious disease epidemiology through reading and critical evaluation of scientific literature in this field. Particular attention is paid to epidemiological methods unique to infectious disease problems.
Network Modeling for Epidemics. Summers at the University of Washington and Virtually (Zoom).
Network Modeling for Epidemics is a 5-day short -course at the University of Washington that provides an introduction to stochastic network models for infectious disease transmission dynamics, with a focus on empirically based modeling of HIV transmission. It is a "hands-on" course, using the EpiModel software package in R [www.epimodel.org]. EpiModel provides a unified framework for statistically based modeling of dynamic networks from empirical data, and simulation of epidemic dynamics on these networks. It has a flexible open-source platform for learning and building several types of epidemic models: deterministic compartmental, stochastic individual-based, and stochastic network models.