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.
Interventions and Calibration
This course is part of Infectious Disease Modelling Specialization
Instructor: Nimalan Arinaminpathy
3,065 already enrolled
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(53 reviews)
What you'll learn
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.
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There are 4 modules in this course
Once you have captured the basic dynamics of transmission using simple mathematical models, it is possible to use these models to simulate the impact of different interventions. You will study 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 (for example, 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.
What's included
5 videos5 readings2 discussion prompts11 ungraded labs
All models answering public health questions first need to be matched, or ‘calibrated’, against real-world data to ensure that model-simulated dynamics are consistent with what is observed. In this module, you will consider basic relationships between models and data. Using the basic SIR model that you've developed so far, you will calibrate this model to epidemic data. Through performing this calibration by hand, you'll gain an understanding of how model parameters can be adjusted so as to order to capture real-world data.
What's included
4 videos1 discussion prompt4 ungraded labs
In practice model calibration for compartmental models is rarely done by hand. Rather, we construct a function that summarises the goodness-of-fit between the model and the data and then use available computer algorithms to maximise this goodness-of-fit. In these next two modules, 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.
What's included
3 videos6 ungraded labs
Please note - learning outcomes are the same across both this and the last module. In practice, model calibration for compartmental models is rarely done by hand. Rather, we construct a function that summarises the goodness-of-fit between the model and the data and then use available computer algorithms to maximise this goodness-of-fit. In these two modules, you'll 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.
What's included
4 videos1 reading2 assignments4 ungraded labs
Instructor
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Imperial College London
Illinois Tech
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