Mathematical modelling is increasingly being used to support public health decision-making in the control of infectious diseases. This specialisation aims to introduce some fundamental concepts of mathematical modelling with all modelling conducted in the programming language R - a widely used application today.
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Mathematical modelling is increasingly being used to support public health decision-making in the control of infectious diseases. This specialisation aims to introduce some fundamental concepts of mathematical modelling with all modelling conducted in the programming language R - a widely used application today.
Mathematical modelling is increasingly being used to support public health decision-making in the control of infectious diseases. This specialisation aims to introduce some fundamental concepts of mathematical modelling with all modelling conducted in the programming language R - a widely used application today.
The specialisation will suit you if you have a basic working knowledge of R, but would also like to learn the necessary basic coding skills to write simple mathematical models in this language. While no advanced mathematical skills are required, you should be familiar with ordinary differential equations, and how to interpret them. You'll receive clear instruction in the basic theory of infectious disease modelling alongside practical, hands-on experience of coding models in the programming language R.
What's inside
Three courses
Developing the SIR Model
Developing the SIR Model
Interventions and Calibration
Interventions and Calibration
Building on the SIR Model
Building on the SIR Model
Learning objectives
- Construct valid mathematical models capturing the natural history of a given infectious disease.
- Implement a mathematical model in r, calibrating it against epidemiological data in order to estimate key model parameters
- Use a calibrated model to create model projections for different intervention scenarios
- Explain the strengths and limitations of a mathematical model in relation to given research and policy questions
- Construct valid mathematical models capturing the natural history of a given infectious disease.
- Implement a mathematical model in r, calibrating it against epidemiological data in order to estimate key model parameters
- Use a calibrated model to create model projections for different intervention scenarios
- Explain the strengths and limitations of a mathematical model in relation to given research and policy questions
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