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

2025-11-10

AI Summary

The transmission dynamics of methicillin-resistant *Staphylococcus aureus* (MRSA) in hospitals remain poorly understood, particularly regarding the distinct roles of hospital-acquired (HA-MRSA) versus community-acquired (CA-MRSA) strains. Method: We developed a compartmental transmission model explicitly distinguishing HA-MRSA and CA-MRSA sources, incorporating susceptible, colonized, infected, and isolated states. Using Bayesian inference with Markov chain Monte Carlo (MCMC), we fitted the model to MRSA surveillance data from multiple hospitals in Edmonton, Canada, and conducted multi-hypothesis submodel comparison to quantify contributions of different transmission pathways and parameter uncertainty. Contribution/Results: Our analysis yielded precise posterior distributions for key parameters—including within-hospital transmission rate and colonization-to-infection progression rate—and identified CA-MRSA importation as a major external driver of nosocomial transmission. The framework provides a generalizable, quantitative modeling paradigm for rigorously evaluating infection control interventions.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Modeling MRSA transmission between hospital and community strains in Edmonton hospitals
• Estimating transmission rates using Bayesian inference with MCMC algorithms
• Developing sub-models to analyze different MRSA colonization origin assumptions

Innovation

• Bayesian compartmental model for MRSA transmission
• MCMC algorithms for parameter estimation
• Sub-models with varying colonization origin assumptions