The Yale School of Public Health is offering a week-long virtual summer course in Public Health Modeling June 21-25. The course will cover the latest analytical and computational techniques in this growing public health field.
The course provides an opportunity to learn from distinguished Yale faculty and to network with an international group of public health researchers. The course is designed to provide researchers, clinicians, industry professionals and policymakers with the systems-based perspective and analytical tools they need to better understand and manage the complex forces that drive health outcomes. Current graduate students, trainees and professionals in any field are welcome to apply.
Modeling – the development and study of systems described by mathematical relationships that portray, account for, and predict patterns of real-world processes – is increasingly used to help experts better understand and manage health outcomes. For instance, transmission models for SARS-CoV-2, the virus that causes COVID-19, have helped scientists predict and respond to the current pandemic. Course topics include prediction and control of infectious disease outbreaks, optimal decision making in health care delivery, and designing interventions to mitigate the effects of drug overdoses.
The instructors – internationally recognized experts in epidemiology, biostatistics and health policy modeling from the Yale School of Public Health – are:
- Forrest W. Crawford, Ph.D., Associate Professor in the Department of Biostatistics, and Associate Professor of Statistics & Data Science, Ecology & Evolutionary Biology, and Management (Operations);
- A. David Paltiel, Ph.D., Professor in the Department of Health Policy and Management and at the Yale School of Management, and Professor in the Institution for Social and Policy Studies;
- Virginia Pitzer, Sc.D., Associate Professor in the Department of Epidemiology of Microbial Diseases.
“This course provides an opportunity to learn the fundamental techniques used by public health modelers. Upon completion, students will be able to better understand the modeling literature and begin to develop and implement models themselves,” said Pitzer.
Online instruction will be from 8 a.m. to noon Monday through Friday beginning June 21. Case studies will be introduced on the first day and will be completed during the week in small groups with a faculty supervisor. Participants will be grouped with others in similar time zones to facilitate discussions and out-of-class work.
Participants should be proficient at undergraduate mathematics, including calculus and basic statistics. Some programming experience (e.g., R, Python, Julia, Matlab or similar flexible programming languages) is required. Students with strong backgrounds in math, statistics, engineering, economics or computer science can take advantage of more advanced course topics.