Bayesian compartmental modelling of MRSA transmission within hospitals in Edmonton, Canada

TL;DR

This paper develops a Bayesian compartmental model to analyze MRSA transmission in Edmonton hospitals, incorporating both hospital- and community-acquired strains, and uses MCMC to estimate transmission parameters.

Contribution

It introduces a novel compartmental model that differentiates between HA-MRSA and CA-MRSA and applies Bayesian inference to estimate transmission dynamics.

Findings

Estimated transmission rates for HA-MRSA and CA-MRSA

Identified key factors influencing MRSA spread in hospitals

Compared different models to understand MRSA origins

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterium that leads to severe infections in hospitalized patients. Previous epidemiological research has focused on MRSA transmission, but few studies have examined the influence of both hospital-acquired MRSA (HA-MRSA) and community-acquired MRSA (CA-MRSA) on MRSA spread in hospitals. In this study, we present a unique compartmental model for studying MRSA transmission patterns in hospitals in Edmonton, Alberta. The model consists of susceptible individuals, patients who have been colonized or infected with HA-MRSA or CA-MRSA, and isolated patients. We first use Bayesian inference with Markov chain Monte Carlo (MCMC) algorithms to estimate the posterior mean of parameters in the full model using data from hospitals in Edmonton. Then we develop multiple sub-models with varying assumptions about the origin of new MRSA colonization. We also estimate transmission rates in hospitals.