Ementorhub

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.

Skills you'll gain

R Programming

Infectious Diseases

Public Health

Statistical Methods

Mathematical Modeling

Epidemiology

Statistical Modeling

Differential Equations

There are 4 modules in 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.

Confronting Models with Data - Part A

Confronting Models with Data - Part B

Confronting models with data – Part C