Title: Infectious Disease Modeling Using Dynamic Compartmental Models in R

Length: 3.5 hours

Level: intermediate

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 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.

Format & prerequisites: Lectures and R exercise session. Basic coding experience in R and differential equations.

Skills covered: modeling dynamic compartmental models of infectious disease transmission and control measures in R

Course objectives: (1) understand the fundamentals of infectious disease epidemiology. (2) how to construct and parameterize dynamic compartmental models of infectious disease transmission and simulate disease control interventions using R. (3) how to choose an appropriate model structure and set of assumptions for a given use case.