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. Reed-Frost dynamics
• 2. Compartmental models
• 3. Simulating the Reed-Frost model
• 4. Montecarlo sampling and the Reed-Frost model under intervention
• 5. Simulating Compartmental models
• 6. Estimating Epidemic models from observations
• 7. A start to stochastic network models
• 8. Multi-species models
• 9. Discrete time Kermack-McKendrick Model
• 10. Fixed points and linear stability
• 11. Temporal forcing and time-dependent parameters
• 12. Stochastic epidemic models
• 13. Metapopulation models
• 14. LTCF application
• 15. Cellular automota
• 16. Bayesian Learning
• 17. Computational Posterior
• 18. Final for Outbreak Science I
• 19. Grad homework one: Binomial proportions and Snow
• 20. Grad Homework Two: Oswego County and the Chi-square test