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Instructor: Nimalan Arinaminpathy
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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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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.
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
Identify the relationship between models and real-world epidemiological data
Incorporate treatment or vaccination into an SIR model, accounting for imperfect efficacy, and for different mechanisms of action
Perform simple calibrations of an SIR model against time-series data, selecting parameters to maximise the fit of the model to the data
Recognise two simple approaches to computer-based model calibration and perform model calibrations under each of these approaches in R.
Distinguish between stochastic and deterministic models, explain when either are appropriate, and perform simple simulations of a stochastic model
Identify where it is important to incorporate population structure in a model and design and simulate a transmission model capturing such structure
Evaluate the assumptions behind the Ross MacDonald model, and code such a model using R to simulate the dynamics of a vector-borne disease
Critically evaluate a modelling study and communicate its strengths and weaknesses to a scientifically literate audience
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. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology.
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While you will not need advanced mathematics for this course, it is important that you feel comfortable with some basic mathematical concepts. You will need a working knowledge of ordinary differential equations (ODEs), and be able to interpret and explain an ordinary differential equation to someone who is not familiar with them.
As all coding activities in this course will be performed using the programming language R, you will benefit from having a working knowledge of this before commencing the specialisation. You will not need to know how to do scientific computing using R (this will be taught), but rather feel comfortable with basic R operations, including importing libraries and running simple commands.
Approximately 4 hours a week for 3 to 4 months
We recommend taking the courses in the order in which they are displayed on the main page of the Specialization
No. Participating in this specialisation will not earn you university credit.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
Financial aid available,