Before revision: This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the COVID-19 outbreak and doing Bayesian inference . Bayesian modeling provides a principled way to quantify uncertainty and incorporate  prior knowledge into the model . What is more, Stan 's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means we can verify whether our inference is reliable. Stan is an expressive probabilistic programing language that abstracts the inference and allows users to focus on the modeling.
After revision: This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the COVID-19 outbreak and doing Bayesian inference . Bayesian modeling provides a principled way to quantify uncertainty and incorporate prior knowledge into the model estimates. Stan is an expressive probabilistic programing language that abstracts the inference and allows users to focus on the modeling.