Methods

Quantitative tools I use in my own work and that come up often in infectious disease epidemiology.

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Causal Inference

Causal inference is a large and rapidly growing field, and it can be hard to know where to start. Brady Neal’s website does a nice job of mapping out the landscape and helping you pick a book based on your background.

• Neal, B. Which causal inference book should you read?

For a hands-on, example-driven introduction, I’ve found Scott Cunningham’s Causal Inference: The Mixtape particularly accessible. It’s freely available online and also published as a book.

• Cunningham, S. Causal Inference: The Mixtape.

Bayesian Statistics

If you’re new to Bayesian statistics, Richard McElreath’s Statistical Rethinking is one of the best places to start. It requires relatively little prior background in statistics, and the lectures and code are freely available online.

• McElreath, R. (2020). Statistical Rethinking, 2nd Edition. Chapman and Hall. GitHub

For those with a stronger statistics background, Gelman et al.’s Bayesian Data Analysis is a classic and goes much deeper:

• Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis, 3rd Edition. Chapman and Hall/CRC. R demos

Finally, this paper by Gelman, Vehtari, and colleagues lays out a principled Bayesian workflow from start to finish. A book version is expected in June 2026.

• Gelman, A., Vehtari, A., Simpson, D., et al. Bayesian Workflow. arXiv. See also: Bayesian Workflow book.

Mathematical Modeling of Infectious Diseases

These are the textbooks I return to most often. Each one takes a somewhat different angle — from applied simulation to formal mathematical analysis to data-driven modeling in R.

• Keeling, M.J. & Rohani, P. Modeling Infectious Diseases in Humans and Animals. A comprehensive guide covering practical applications, simulation techniques, and model analysis. Website
• Diekmann, O. & Heesterbeek, J.A.P. Mathematical Epidemiology of Infectious Diseases. Focuses on model building, analysis, and interpretation; suited for a more formal mathematical approach. Springer
• Anderson, R.M. & May, R.M. Infectious Diseases of Humans: Dynamics and Control. A foundational text in the field, often cited for its deep theoretical treatment of host–pathogen dynamics.
• Bjørnstad, O.N. Epidemics: Models and Data using R. Combines epidemic modeling with practical implementation in R — a good entry point if you want to learn by doing. Springer

Coding

Most of us in epidemiology and infectious disease modeling are largely self-taught programmers. These resources fill gaps that formal training often leaves behind.

R programming and visualization

• Wilke, C.O. Fundamentals of Data Visualization. A thoughtful guide to designing clear, effective figures — useful well beyond R.

General coding

• Athalye, A., Zhu, J. & Hålaj, A. The Missing Semester of Your CS Education. Covers the practical tools — the shell, version control, debugging, and more — that most curricula skip over but that you’ll use every day.