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Course Description


Modeling is the process of formalizing ideas about how a system works, learning about its dynamics, and making predictions about the future. Modern public health research and practice can utilize models to better understand and manage dynamic processes - from optimal decision-making in healthcare delivery and design of clinical trials, to prediction and control of infectious disease outbreaks, to mitigating the effects of drug overdoses.

Modeling has been key to anticipating and responding to the COVID-19 pandemic. Models for transmission of SARS-CoV-2, the virus that causes COVID-19, have helped the world predict and respond to the pandemic. Today’s public health analysts and decision-makers need to understand how such models are developed, what their strengths and weaknesses are, and how to interpret their output.

To better prepare the community of public health researchers and practitioners to meet the world’s most significant challenges in epidemiology and health policy, the Yale School of Public Health is offering a five-day course in Public Health Modeling. This course provides hands-on training in the study and management of systems and processes that impact the health of individuals and populations. Course instructors are Yale faculty experts in epidemiology, biostatistics, health policy and health care operations, and public policy.

Intended Audience

Researchers, healthcare workers, public health officials, and policymakers interested in using modeling to solve challenges in public health, policy, and biomedical science. We welcome current graduate students, trainees, and professionals in any field.


Attendees should be familiar with undergraduate mathematics, including calculus, and basic statistics. Some experience with a flexible programming language (e.g. R, Python, Julia, MATLAB) is required. Attendees with a strong quantitative background in mathematics, statistics, engineering, economics, or computer science will be able to take advantage of more advanced course topics.

Learning Objectives

Upon completion of the course, students will be able to:

  1. Formalize hypotheses and intuition about health system dynamics into coherent mathematical models
  2. Construct models of systems and processes in epidemiology, health economics/policy, and biomedical science
  3. Implement models in the R programming language
  4. Calibrate models using rigorous tools from statistical inference
  5. Generate predictions from models