Compartmental modelling is a cornerstone of mathematical modelling of infectious diseases and this course will introduce some of the basic concepts in building compartmental models, including how to interpret and represent rates, durations and proportions. You'll learn to place the mathematics to one side and concentrate on gaining intuition into the behaviour of a simple epidemic, and be introduced to further basic concepts of infectious disease epidemiology, such as the basic reproduction number (R0) and its implications for infectious disease dynamics. To express the mathematical underpinnings of the basic drivers that you study, you'll use the simple SIR model, which, in turn, will help you examine different scenarios for reproduction numbers. Susceptibility to infection is the fuel for an infectious disease, so understanding the dynamics of susceptibility can offer important insights into epidemic dynamics, as well as priorities for control.
课程信息
您将学到的内容有
Construct valid mathematical models capturing the natural history of a given infectious disease
Interpret compartmental models in terms of rates, proportions and delays
Describe the fundamental processes driving the dynamics of an SIR epidemic and show their relation to important concepts
Explain mechanisms by which susceptibility can change over time and develop a simple SIR model to account for these under given parameters
您将获得的技能
- Mathematical Model
- Infectious Diseases
提供方

伦敦帝国学院
Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.
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Modelling the Basics
Compartmental modelling is a cornerstone of mathematical modelling of infectious diseases. You will be introduced to some of the basic concepts in building compartmental models, including how to interpret and represent rates, durations and proportions in such models. This work lays the foundations for modelling the dynamics of infectious disease transmission.
Anatomy of an Epidemic
You will be placing the mathematics to one side and concentrating on gaining intuition into the behaviour of a simple epidemic of a perfectly immunising infection in a stable population. You will also study further basic concepts of infectious disease epidemiology, including the basic reproduction number (R0), and its implications for infectious disease dynamics.
Combining Modelling and Insights
You will now consolidate the insights that you have gained over the past two modules to express the mathematical underpinnings of the basic drivers that have been examined. You will use the simple SIR model that you already developed in module 1 to examine different scenarios for reproduction numbers.
Dynamics of Susceptibles
Susceptibility to infection is the fuel for an infectious disease; understanding the dynamics of susceptibility can offer important insights into epidemic dynamics, as well as priorities for control. In this module, building on the basic SIR model that you have coded so far, you will cover three important mechanisms by which susceptibility can change over the course of an epidemic: (i) population turnover, (ii) vaccination, (iii) immunity waning over time.
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- 5 stars89.56%
- 4 stars8.26%
- 3 stars0.43%
- 1 star1.73%
来自DEVELOPING THE SIR MODEL的热门评论
A truly wonderful course that allows the understanding of disease transmission through mathematical tools
Achievable targets, constant feedback, great balance between exercises, video, reading make this course truly rewarding and engaging. Thanks!
This is an excellent course. It covers a lot of material, but is very well organized. Gives a great introduction to infectious disease modeling that is intuitive and easy to follow.
Well structured, clearly presented course. The online R platform is not very convenient compared to a desktop IDE.
关于 Infectious Disease Modelling 专项课程
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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