Mathematical modelling is being increasingly used to inform public health decisions, with a recent example being the use of models during the COVID-19 pandemic to advise policy around what control measures were required and what the future epidemic trend might look like under different scenarios.
By dividing a population into categories, such as "susceptible" or "infectious", we can then consider the rate of movement of individuals from one category to another, based on factors like social contact patterns, risk of disease transmitting from one person to another, or time from infection to recovery. From this, we can write down equations that describe these movements on a population-scale, which can be analysed to draw conclusions to important questions, such as:
• If a new disease emerges in the population, will it take off or die out?
• What public health measures will be most effective?
• What level of vaccination is required to prevent an epidemic?
This course provides an introduction to the key mathematical concepts required for building and analysing mathematical models of infectious disease transmission.