28 June 2026, 1:30 PM – 4:55 PM | 13:30 – 16:55 (local time)
Faculty
- Theresa (Tess) Ryckman, PhD – Assistant Professor, Division of Infectious Diseases, Johns Hopkins School of Medicine, United States
- Kyueun Lee, PhD – Endowed Assistant Professor of Health Decision Science, Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington School of Pharmacy, United States
Course Overview
Mathematical models are a useful tool to understand disease trends and evaluate health policies. Unlike non-communicable diseases, the risk of acquiring an infectious disease depends on the population size of individuals infected and their mixing patterns with the susceptible population. These transmission-dynamic components necessitate specialized methods for modeling infectious diseases.
This course focuses on a commonly used class of infectious disease models: dynamic compartmental models governed by differential equations. The course is structured in three parts: (1) an overview of basic concepts in infectious disease epidemiology, including the basic reproductive number, contact matrices, and herd immunity; (2) translating these concepts into model assumptions and parameters in R; and (3) exploring an infectious disease cost-effectiveness case study.
Participants will begin with the classic SIR (susceptible–infectious–recovered) model and examine extensions that incorporate additional features such as latency and waning immunity. The course will include simulations of disease control interventions such as quarantine and vaccination, demonstrating how these strategies influence disease dynamics. Participants will also learn how to incorporate costs into dynamic compartmental models to conduct cost-effectiveness analyses.
Through this course, participants will gain both an understanding of the theoretical foundations of dynamic compartmental models and practical experience implementing them in R for real-world applications. The course will also briefly discuss limitations of dynamic compartmental modeling and alternative modeling approaches that may be more appropriate in certain contexts.
Learning Objectives
Participants will learn how to:
- Understand the fundamentals of infectious disease epidemiology
- Construct and parameterize dynamic compartmental models of infectious disease transmission using R
- Gain experience simulating and assessing the cost-effectiveness of disease control interventions
- Choose an appropriate model structure and set of assumptions for a given use case
Course Format
The course will include a mix of interactive lectures, code demonstrations, and hands-on exercises completed during the session.
Participants will work through examples covering multiple infectious diseases and intervention scenarios, with guided R exercises to reinforce key concepts. The session will also include discussion of modeling approaches, assumptions, and best practices.
Participant Requirements
Participants should have a basic understanding of differential equations and familiarity with R coding. Previous experience in infectious disease modeling is not required.
Participants should bring a laptop with R installed. Code and materials will be provided for use during and after the course. Short background readings will be shared in advance.