[After reading through this module, students should have an understanding of contact dynamics in a population with age structure (eg; kids and adults). You should understand how population age structure can affect the spread of infectious disease. You should be able to write down the differential equations of a simple SIR disease model with age structure, and you will learn in this module how to solve those differential equations in R to obtain the model estimate of the epidemic curve]
In a previous module I discussed epidemic modelling with a simple Susceptible, Infected, Recovered (SIR) compartmental model. The model presented had only a single age class (ie; it was homogenous with respect to age). But in reality, when we consider disease transmission, age likely does matter because kids usually make more contacts during the day than adults. The differences in contact patterns between age groups can have quite a profound impact on the model estimate of the epidemic curve, and also have implications for development of optimal disease intervention strategies (like age-targeted vaccination, social distancing, or closing schools). Continue reading →
In another module on this site I describe how an epidemic for certain kinds of infectious diseases (like influenza) can be modelled with a simple Susceptible, Infectious, Recovered (SIR) model. Readers who have not yet been exposed to calculus (such as junior or senior high school students) may have been daunted by the system of differential equations shown in that post. However, with only a small amount of programming experience in R, students without calculus can still easily model epidemics, or any other system that can be described with a compartmental model. In this post I will show how that is done. Continue reading →
[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]
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.
[After you have read through this module, and have downloaded and worked through the provided R examples, you should be proficient enough in R to be able to download and run other R scripts that will be provided in other posts on this site. You should understand the basics of good programming practices (in any language, not just R). You will also have learned how to read data in a file into a table in R, and produce a plot.]
I have programmed in many different computing and scripting languages, but the ones I most commonly use on a day to day basis are C++, Fortran, Perl, and R (with some Python, Java, and Ruby on the side). In particular, I use R every day because it is not only a programming language, but also has graphics and a very large suite of statistical tools. Connecting models to data is a process that requires statistical tools, and R provides those tools, plus a lot more.
Unlike SAS, Stata, SPSS, and Matlab, R is free and open source (it is hard to beat a package that is more comprehensive than pretty much any other product out there and is free!).