Further Reading

Bayesian inference

• Gelman, A. et al. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC. The standard reference for Bayesian methods.
• McElreath, R. (2020). Statistical Rethinking (2nd ed.). CRC Press. An accessible introduction to Bayesian modelling with a focus on building intuition.

Infectious disease modelling

• Keeling, M.J. & Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals. Princeton University Press. Comprehensive treatment of compartmental models and their analysis.
• Vynnycky, E. & White, R.G. (2010). An Introduction to Infectious Disease Modelling. Oxford University Press. Accessible introduction to modelling with a focus on practical application.

Julia

• Julia manual — official language documentation
• Think Julia — introduction to programming using Julia
• Julia learning resources — curated index of tutorials, books, videos, and courses
• Julia Data Science — practical introduction to data analysis in Julia
• Julia for epidemiologists — curated resources for those coming from R/Python in epi contexts

Probabilistic programming

• Turing.jl tutorials — worked examples from simple to advanced
• van de Meent, J.-W. et al. (2018). An Introduction to Probabilistic Programming. arXiv:1809.10756.

MCMC and computational methods

• Brooks, S. et al. (2011). Handbook of Markov Chain Monte Carlo. Chapman & Hall/CRC.
• Betancourt, M. (2017). A Conceptual Introduction to Hamiltonian Monte Carlo. arXiv:1701.02434. Excellent explanation of the NUTS sampler used throughout this course.