Building on the SIR Model

The other two courses in this specialisation have focused on performing deterministic modelling - that is, the epidemic outcome is predictable as all parameters are fully known. However, there are many cases, especially in the early stages of an epidemic, where chance events can be influential in the future of an epidemic. In this module, you will be introduced to some examples of such ‘stochasticity’, as well as, simple approaches to modelling these epidemics using R.

In the basic deterministic SIR model, all susceptible individuals in a population are subject to the same risks of infection. However, there are many important infectious diseases where certain groups of the population account for a disproportionate amount of transmission: these are not always the same groups that bear the greatest amount of morbidity and mortality. In this module, you will examine how to model infections for which such ‘population structure’ plays an important role in the transmission dynamics.

Many important diseases are not directly transmitted between hosts, but depend on ‘vectors’ to pass infection between hosts, for example biting insects. It is important to be able to extend the modelling approaches you have studied so far to capture these more complex forms of natural history. In this module, you will learn some of the basic approaches to modelling vector-borne diseases, including the Ross-McDonald model, which is a framework that provides an important foundation for such diseases.