Modeling contact networks of patients and MRSA spread in Swedish hospitals

TL;DR

This study models hospital contact networks to understand MRSA spread, revealing how heterogeneity influences epidemic growth and identifying effective control strategies like screening over hygiene improvements.

Contribution

It introduces a high-resolution spatiotemporal contact network model capturing heterogeneity in hospital interactions for MRSA transmission.

Findings

Heterogeneous contact patterns lead to super-spreader patients.

Epidemic growth follows a polynomial trend.

Screening outperforms hygiene measures in controlling outbreaks.

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection that only in the European Union affects about 150,000 patients and causes extra costs of 380 million Euros annually to the health-care systems. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA through modeling is essential to identify the causal mechanisms driving the epidemics and to generalize conclusions to different contexts. We develop an innovative high-resolution spatiotemporal contact network model of interactions between patients to reproduce the hospital population in the context of the Stockholm County in Sweden and simulate the spread of MRSA within this population. Our model captures the spatial and temporal heterogeneity caused by human behavior and by the dynamics of mobility within wards and hospitals. We estimate that in this population the epidemic threshold is at about 0.008. We also identify that these heterogeneous contact patterns cause the emergence of super-spreader patients and a polynomial growth of the epidemic curve. We finally study the effect of standard intervention control strategies and identify that screening is more effective than improved hygienic in order to cause smaller or null outbreaks.