[After reading through this module you should have an intuitive understanding of how infectious disease spreads in the population, and how that process can be described using a compartmental model with flow between the compartments. You should be able to write down the differential equations of a simple disease model, and you will learn in this module how to numerically solve those differential equations in R to obtain the model estimate of the epidemic curve]
An excellent reference book with background material related to these lectures is Mathematical Epidemiology by Brauer et al.
Contents:
- Introduction
- Basic dynamics of infectious disease spread
- The SIR compartmental model of disease spread
- The SIR model system of equations
- Numerically solving the SIR model system of equations in R
- R code to model an influenza pandemic with an SIR model
- Further things you can explore
- Summary
Introduction
Models of disease spread can yield insights into the mechanisms and dynamics most important to the spread of disease (especially when the models are compared to epidemic data). With this improved understanding, more effective disease intervention strategies can potentially be developed. Sometimes disease models are also used to forecast the course of an epidemic, and doing exactly that for the 2009 pandemic was my introduction to the field of computational epidemiology.
There are lots of different ways to model epidemics, and there are several modules on this site on the topic, but let’s begin with one of the simplest epidemic models for an infectious disease like influenza: the Susceptible, Infected, Recovered (SIR) model.
