Skip to Main Content

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.

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 developing, understanding, and interpreting models. Course instructors are Yale faculty experts in epidemiology, biostatistics, health policy and health care operations, and public policy.


The Mathematical Portrayal of Public Health Systems: A Look into The YSPH Public Health Modeling Unit

In the face of rapidly unfolding and life-threatening diseases that affect large populations, David Paltiel, co-director of YSPH’s Public Health Modeling Unit, and his colleagues are often asking the question of the value of information. “How do you know when it makes sense to take the time and go to the expense of acquiring new information as opposed to making a decision today,” Paltiel asks, “knowing full well that the decision to wait is one that carries its own costs and consequences?”

In this video, four members of the YSPH Public Health Modeling Unit unveil the critical role of employing mathematics to simulate the complex dynamics of epidemics, disease impact, and human behavior in public health systems. Discover how these experts navigate the challenges of urgent decision-making with limited data, financial constraints, and ethical dilemmas, shifting away from traditional observational studies towards innovative modeling techniques.

As the pace of global health issues accelerates, learn why the future of public health demands a versatile toolkit that integrates modeling with epidemiology, biostatistics, and economics to shape data-driven leadership and strategic responses in the face of emerging health threats.

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