Interventions and Calibration
About this course
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.
90/100
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What you'll learn
- Incorporate intervention and vaccination compartments into SIR models
- Model different types of vaccines including leaky vaccines and their effects on disease transmission
- Calibrate epidemiological models to real epidemic data manually
- Implement least-squares calibration methods for infectious disease models in R
- Implement maximum-likelihood calibration methods for infectious disease models in R
- Understand relationships between model parameters and real-world epidemic data
Course objectives
- Build models that represent the effects of disease interventions and vaccination programs
- Gain practical experience calibrating models to epidemic data
- Apply computational calibration techniques to epidemiological models
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