Infectious Disease Modeling Using Dynamic Compartmental Models in R

Length: 3.5 Hours

Level: Intermediate

August 14, 2024


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 people infected with that disease and their mixing patterns with the susceptible population. These transmission-dynamic components of infectious diseases necessitate a special set of methods for modeling infectious diseases.

In this course, we focus on a commonly used form of infectious disease models: dynamic compartmental models governed by differential equations. Our course has two parts: 1) providing a brief overview of basic concepts in infectious disease epidemiology – such as the basic reproductive number, contact matrixes, and herd immunity, and 2) demonstrating how to translate these concepts into model assumptions and parameters in R. In the model implementation section, we will start with the classic “SIR” (susceptible-infectious-recovered) model and describe embellishments to this model that capture additional characteristics of infectious diseases, such as latency and waning immunity. We will simulate disease control interventions such as quarantine and vaccination, highlighting how they alter the disease dynamics. Through this course, participants will learn both the theoretical underpinnings of dynamic compartmental models as well as how to implement them in R for real-world applications. The course will include hands-on examples covering a variety of infectious diseases and interventions. We will also briefly discuss the limitations of dynamic compartmental modeling and other model structures that may be more appropriate for certain diseases or situations.


Kyueun Lee,PhD, is an Assistant Professor of Health Decision Science at the Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, at the University of Washington School of Pharmacy. Her research focuses on using disease simulation models to forecast the future burden of disease and evaluate the value of innovative health technology, primarily in the field of respiratory infectious diseases. Her two recent research projects examined the impact of COVID-19 non-pharmaceutical interventions on the population immunity and future flu epidemics in the United States and estimated the impact of new vaccine technologies (e.g. cell-based, recombinant, and mRNA vaccines) on the future burden of respiratory infectious diseases such as flu and RSV, using mathematical models.



Dr. Tess Ryckman is an Instructor in the Division of Infectious Diseases at the Johns Hopkins School of Medicine. Her research involves the use of mechanistic modeling and cost-effectiveness analysis to inform policy decisions through deeper understanding of the natural history and transmission of infectious diseases. She is currently engaged in several projects related to tuberculosis epidemiology and the economics of strategies to reduce population-level TB burdens and improve experiences and outcomes for individuals with TB.






USD 425 for Non-Members
USD 280 for Members
USD 150 for Trainee, LMIC or Bridge Members


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