With the world in the grip of the coronavirus pandemic, there has been a surge of interest in scientific modelling of the outbreak. On this course, you’ll explore the social, economic, and political factors in the spread of a pandemic such as COVID-19, examining how scientists try to forecast the spread and severity of epidemics, and what we can and can’t know.
You’ll use interactive graphical programs to explore the dynamics of epidemics, learning how to critique the underlying models, and how science and computer models can support policymakers in times of pandemic crisis.
- COVID-19: a brief description, the problem, and what we need to know?
- The nature of prediction – why things can or can’t be predicted
- Modelling – creating and using computational models
- Policy– how is policy made? How does policy interface to science?
- Complex systems science for exploring and planning the future
This course is for anyone wanting to make sense of the conflicting information around COVID-19. This includes scientists, health professionals, policy planners, or just interested citizens.
By the end of the course, you‘ll be able to…
- Explain what models are and how they are used in policy making
- Model the COVID-19 pandemic using computational methods including time series and implementations of the SIR (Susceptible, Infected, Recovered) model
- Evaluate and critique models and computer modelling in a policy context
- Explain the difference between macro-modelling at the level of a whole population, micro-modelling at the level of the individual, and meso-modelling at the level of organisations and social events.
- Evaluate reports used to guide policy and explain how policy makers must deal with conflicting scientific evidence.