Dynamic contact networks of patients and MRSA spread in hospitals

Luis E C Rocha,Tom Lenaerts,Anna Thorson,Vikramjit Singh,Fredrik Liljeros,Markus Esch

doi:10.1038/s41598-020-66270-9

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.

Highlights

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection
The transmission between a patient and a health care workers (HCW) is more likely in certain wards, the mobility of patients between wards or hospitals[15,16] creates the missing links sustaining the spread of HA-MRSA across the hospitalised population
While most hospitals are peripheral and potentially less likely to be affected by an epidemic outbreak, larger hospitals behave like hubs with high influx of patients and internal transfers between wards or clinics

Summary

Introduction

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. Identifying which contact patterns (or network structures17) regulate the propagation of the infection is the first step to better understand www.nature.com/scientificreports the spread potential of MRSA, and to develop efficient strategies to reduce the incidence in endemic areas and to avoid potential outbreaks in low-prevalence contexts[18]. Contrary to agent-based models, our network model is deterministic and captures the exact temporal and spatial heterogeneities contained in the real-data This methodology focused on the actual patients naturally captures the real-world contact patterns and skips several assumptions on the dynamics of patients as for example length-of-stay, re-admittance, mobility between wards and hospitals, hospital and ward sizes, occupancy levels, etc. Long hospital stays increase the risk of infection per stay and may compensate a relatively low risk of transmission per contact, mobility between hospitals and re-hospitalisation play crucial roles to spread the infection across the system and to the community, and differences in the hospital structure (wards) may shape the spread within hospitals

Methods

Results

Conclusion