Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

Philippe Vanhems, Alain Barrat, Ciro Cattuto, Jean-François Pinton, Nagham Khanafer, Corinne Régis, Byeul-a Kim, Brigitte Comte, Nicolas Voirin, PLoS ONE 8(9), e73970 (2013)

Background

Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures.

Methods and Findings

We used wearable sensors to detect close-range interactions (“contacts”) between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders.

Conclusions

Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.


URL: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0073970

PDF: http://www.plosone.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0073970&representation=PDF

BIBTEX:

@article{10.1371/journal.pone.0073970,
    author = {Vanhems, , Philippe AND Barrat, , Alain AND Cattuto, , Ciro AND Pinton, , Jean-François AND Khanafer, , Nagham AND Régis, , Corinne AND Kim, , Byeul-a AND Comte, , Brigitte AND Voirin, , Nicolas},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors},
    year = {2013},
    month = {09},
    volume = {8},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0073970},
    pages = {e73970},
    abstract = {Background

Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures.

Methods and Findings

We used wearable sensors to detect close-range interactions (“contacts”) between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders.

Conclusions

Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.

}, number = {9}, doi = {10.1371/journal.pone.0073970} }

PUBLICATIONS

Estimating the epidemic risk using non-uniformly sampled contact data
Recalibrating disease parameters for increasing realism in modeling epidemics in closed settings
School closure policies at municipality level for mitigating influenza spread: a model-based evaluation
Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants’ attitudes
Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk
How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?
Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors
Epidemic risk from friendship network data: an equivalence with a non-uniform sampling of contact networks
Compensating for population sampling in simulations of epidemic spread on temporal contact networks
Enhancing the evaluation of pathogen transmission risk in a hospital by merging hand-hygiene compliance and contact data: a proof-of-concept study
Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers
Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists
Combining High-Resolution Contact Data with Virological Data to Investigate Influenza Transmission in a Tertiary Care Hospital
Mental health and social networks in early adolescence: A dynamic study of objectively-measured social interaction behaviors
Mitigation of infectious disease at school: targeted class closure vs school closure
How memory generates heterogeneous dynamics in temporal networks
Contact patterns among high school students
Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach
Measuring contact patterns with wearable sensors: methods, data characteristics and applications to data-driven simulations of infectious diseases
Bootstrapping under constraint for the assessment of group behavior in human contact networks
Immunization strategies for epidemic processes in time-varying contact networks
Activity clocks: spreading dynamics on temporal networks of human contact
Gender homophily from spatial behavior in a primary school: a sociometric study
Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors
Empirical temporal networks of face-to-face human interactions
New Insights and Methods for Predicting Face-To-Face Contacts
Time-varying Social Networks in a Graph Database – A Neo4j Use Case
Temporal networks of face-to-face human interactions
An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
Fingerprinting temporal networks of close-range human proximity
Digital Epidemiology
Random Walks on Temporal Networks
The making of Sixty-Nine Days Of Close Encounters At The Science Gallery.
High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School.
Simulation of an SEIR Infectious Disease Model on the Dynamic Contact Network of Conference Attendees.
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks.
Close Encounters in a Pediatric Ward: Measuring Face-to-Face Proximity and Mixing Patterns with Wearable Sensors.
What’s in a Crowd? Analysis of Face-to-Face Behavioral Networks.
Wearable Sensor Networks for Measuring Face-to-Face Contact Patterns in Healthcare Settings.
Social Dynamics in Conferences: Analysis of Data from the Live Social Semantics Application.
Providing Enhanced Social Interaction Services for Industry Exhibitors at large Medical Conferences.
Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks.
Semantics, Sensors, and the Social Web: The Live Social Semantics Experiments.
The Live Social Semantics Application: a Platform for Integrating Face-to-Face Presence with On-Line Social Networking
Live Social Semantics
High Resolution Dynamical Mapping of Social Interactions With Active RFID.