🤖 AI Summary
This study addresses the limited Bayesian statistical background among mathematical epidemiologists by presenting a systematic Bayesian inference framework for the classic SIR model. Starting from the underlying disease transmission dynamics, the authors derive the likelihood function, specify biologically plausible prior distributions for the transmission and recovery rates, and implement posterior sampling using the Metropolis–Hastings algorithm. The work integrates Bayesian inference and Markov chain Monte Carlo (MCMC) methods into a self-contained, accessible tutorial that substantially lowers the technical barrier for newcomers. By offering a reproducible and user-friendly implementation, this approach provides an entry-level paradigm that effectively balances pedagogical clarity with practical applicability for parameter estimation in infectious disease modeling.
📝 Abstract
This lecture note provides a self-contained introduction to Bayesian inference and Markov Chain Monte Carlo (MCMC) methods for parameter estimation in epidemic models. Using the classical Susceptible-Infectious-Recovered (SIR) compartmental model as a running example, we derive the likelihood function from first principles, specify priors on the transmission and recovery parameters, and implement the Metropolis-Hastings algorithm to sample from the posterior distribution. The note is aimed at graduate students and researchers in mathematical epidemiology with limited prior exposure to Bayesian statistics.