Outbreak science and public health forecasting

This course aims to introduce students to models that describe the spread of a pathogen through a population, and how models can support public health decisions. The course will be split into four parts: (1) the factors that motivate public health actions, (2) epidemic models such as the Reed-Frost and SIR, (3) statistical time series and forecasts, (4) a focus on ensemble building. Students will be expected to complete mathematical/statistical exercises and write code that simulates infectious processes.

Table of Contents:

• 1. Chapter One - Reed-Frost dynamics
• 2. Chapter Two - Compartmental models
• 3. Chapter Three - Simulating the Reed-Frost model
• 4. Chapter Four - Montecarlo sampling and the Reed-Frost model under intervention
• 5. Chapter Six - Simulating Compartmental models
• 6. Estimating Epidemic models from observations
• 7. A start to stochastic network models