ModelingR ProgrammingMathematical ModelInfectious Diseases
您将学到的内容有
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
您将获得的技能
关于此 专项课程
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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.
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.
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中级
需要一些相关领域经验。需要一些相关经验。
完成时间大约为3 个月
建议 5 小时/周
英语（English）
字幕：英语（English）
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
需要一些相关领域经验。需要一些相关经验。
完成时间大约为3 个月
建议 5 小时/周
英语（English）
字幕：英语（English）
此专项课程包含 3 门课程
Developing the SIR Model
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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.
Interventions and Calibration
This course covers approaches for modelling treatment of infectious disease, as well as for modelling vaccination. Building on the SIR model, you will learn how to incorporate additional compartments to represent the effects of interventions, such the effect of vaccination in reducing susceptibility. You will learn about ‘leaky’ vaccines and how to model them, as well as different types of vaccine and treatment effects. It is important to consider basic relationships between models and data, so, using the basic SIR model you have developed in course 1, you will calibrate this model to epidemic data. Performing such a calibration by hand will help you gain an understanding of how model parameters can be adjusted in order to capture real-world data. Lastly in this course, you will learn about two simple approaches to computer-based model calibration - the least-squares approach and the maximum-likelihood approach; you will perform model calibrations under each of these approaches in R.
Building on the SIR Model
The other two courses in this specialisation require you to perform deterministic modelling - in other words, the epidemic outcome is predictable as all parameters are fully known. However, this course delves into the many cases – especially in the early stages of an epidemic – where chance events can be influential in the future of an epidemic. So, you'll be introduced to some examples of such ‘stochasticity’, as well as simple approaches to modelling these epidemics using R. You will examine how to model infections for which such ‘population structure’ plays an important role in the transmission dynamics, and will learn some of the basic approaches to modelling vector-borne diseases, including the Ross-McDonald Model.
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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How long does it take to complete the Specialization?
Approximately 4 hours a week for 3 to 4 months
完成专项课程需要多长时间？
We recommend taking the courses in the order in which they are displayed on the main page of the Specialization
Will I earn university credit for completing the Specialization?
No. Participating in this specialisation will not earn you university credit.
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