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.
Este curso forma parte de Programa especializado: Infectious Disease Modelling
ofrecido por
Programa especializado: Infectious Disease ModellingImperial College LondonAcerca de este Curso
Qué aprenderás
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.
Habilidades que obtendrás
- Mathematical Model
- Infectious Diseases
ofrecido por
Imperial College London
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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Programa - Qué aprenderás en este curso
Modelling Interventions
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.
Confronting Models with Data - Part A
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.
Confronting Models with Data - Part B
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.
Confronting models with data – Part C
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.
Reseñas
- 5 stars74,46 %
- 4 stars19,14 %
- 3 stars2,12 %
- 2 stars4,25 %
Principales reseñas sobre INTERVENTIONS AND CALIBRATION
Very useful course.. I have learnt many things useful for my career
Good content but some exercises and final quiz are designed poorly that sometimes don't even test your learning.
A great learning experience, have to struggle a lot for the quiz, But in the end it helps to get better understanding of the concept and practice.
Stuck in last quiz for many hours, dig in many forums. Finally learn in-depth how and why model structure be like that. 5/5 would loss in thought again.
Acerca de Programa especializado: Infectious Disease Modelling
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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