Dynamic contact networks of patients and MRSA spread in hospitals

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.

Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus

“…The dataset contains admission and discharge records of 743,599 distinct patients from 66 hospitals (271 clinics, 1041 wards) in Stockholm County, Sweden (Jarynowski and Liljeros, 2015; Rocha et al, 2016), spanning over 3500 continuous days during the 2000s. The exact dates and ward types are confidential for the protection of patient privacy.…”

“…In light of this situation, mathematical modeling offers an alternative approach for locating individuals with a high probability of colonization and guiding the targeted deployment of laboratory testing (Grundmann and Hellriegel, 2006; van Kleef et al, 2013; Opatowski et al, 2011). However, this inference problem is again complicated by the unobserved stealth transmission dynamics that occurs in the highly complex time-varying contact networks of the real world (Donker et al, 2010; Vanhems et al, 2013; Jarynowski and Liljeros, 2015; Obadia et al, 2015a; Obadia et al, 2015b; Rocha et al, 2016; Nekkab et al, 2017; Duval et al, 2018).…”

Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19

“…Although various empirical data were collected over the last decades to analyze social networks, we still need more empirical data to analyze the actual contact networks and events of the COVID-19 pandemic [43][44][45]. This specific "corona network" may have different characteristics than other social networks, such as information exchange networks, and may also be different from other virus networks.…”

Prevalence of MRSA nasal carriage among pregnant women in Copenhagen

“…It has been suggested that MRSA admission screening, especially in high risk settings such as intensive care units, could be useful for early recognition of asymptomatic MRSA carriage and outbreak prevention [ 3 , 15 , 16 ]. The aim of this study was to determine the prevalence of MRSA nasal carriage in pregnant women within the Hvidovre Hospital catchment area and to clarify if MRSA screening during pregnancy could prevent NICU outbreaks.…”