Resources

Curated reading and tooling for after the workshop. Pick one book and one package this week, not all of them.

Tutorials and self-paced courses

• Epiverse-TRACE tutorials — tutorials-early, -middle, -late. The same packages we use, taught at three levels.
• The Epidemiologist R Handbook — practical recipes for outbreak data work. The first book to reach for.
• RECON Learn — case-study driven; many start from a line list and end at an Rₜ estimate.

Books

• Anderson, R. M. & May, R. M. (1991). Infectious Diseases of Humans. Oxford University Press. The foundational text — where most of the R₀ values we quoted come from.
• Vynnycky, E. & White, R. G. (2010). An Introduction to Infectious Disease Modelling. OUP. The most accessible classroom textbook.
• Keeling, M. J. & Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals. Princeton. Heavier on dynamics, lighter on code.

Live tools

• AMCHSS COVID-19 Dashboard — Session 5’s case study. Open it, click around, then read the source on the dashboard’s About page.
• outbreaks — every line list you’ll need to practise on.
• The Epidemiologist R Handbook — Case studies — paired with theory.

Going further — Bayesian and stochastic

• Stan boarding-school SIR case study — the canonical worked example for fitting an SIR to real data with Bayesian methods.
• EpiNow2 — production-grade Bayesian Rₜ with reporting delays.
• epichains — branching-process stochastic models when outbreaks are small.
• odin2 — a DSL for compiled, faster stochastic compartmental models.

R-Ladies + community talks

• Gazzelloni, F. (2023). Deterministic SIR model with R. R-Ladies Rome — github.com/Fgazzelloni/sir-model-with-R · slides.
• Buros, J. (2023). Bayesian SIR model with R — github.com/jburos/r-ladies-sir-model.

Workshop homework

Pick one disease you care about, one dataset you have access to, and one method from today. Spend an evening trying it. Email questions.