**[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]**

**Contents:**

- Introduction
- Population contact patterns
- An example of a contact matrix: kids and adults
- SIR model with age structure
- The reproduction number of the age structured SIR model
- R code for simulating an age structured SIR model
- Other kinds of class structure
- Things to try

**Introduction**

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).

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