Bayesian Evidence Synthesis for Modeling SARS-CoV-2 Transmission

TL;DR

This paper develops a Bayesian framework for modeling SARS-CoV-2 transmission, integrating multiple data sources and advanced inference methods to improve estimates of total infections and understand epidemic dynamics.

Contribution

It introduces a Bayesian epidemic model that accounts for under-reporting, compares inference techniques, and uses phase plane analysis for better decision support.

Findings

Hamiltonian Monte Carlo provides robust inference.

Mobility data improves infection rate prediction.

Informative priors enhance model flexibility.

Abstract

The acute phase of the Covid-19 pandemic has made apparent the need for decision support based upon accurate epidemic modeling. This process is substantially hampered by under-reporting of cases and related data incompleteness issues. In this article we adopt the Bayesian paradigm and synthesize publicly available data via a discrete-time stochastic epidemic modeling framework. The models allow for estimating the total number of infections while accounting for the endemic phase of the pandemic. We assess the prediction of the infection rate utilizing mobility information, notably the principal components of the mobility data. We evaluate variational Bayes in this context and find that Hamiltonian Monte Carlo offers a robust inference alternative for such models. We elaborate upon vector analysis of the epidemic dynamics, thus enriching the traditional tools used for decision making. In particular, we show how certain 2-dimensional plots on the phase plane may yield intuitive information regarding the speed and the type of transmission dynamics. We investigate the potential of a two-stage analysis as a consequence of cutting feedback, for inference on certain functionals of the model parameters. Finally, we show that a point mass on critical parameters is overly restrictive and investigate informative priors as a suitable alternative.