title: Estimating potential infection transmission routes in hospital wards using wearable proximity sensors.
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.
  
  
  RESULTS
  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.
authors.affiliation: Hospices Civils de Lyon, Hôpital Edouard Herriot, Service d'Hygiène, Epidémiologie et Prévention, Lyon, France ; Université de Lyon, université Lyon 1, CNRS UMR 5558, laboratoire de Biométrie et de Biologie Evolutive, Equipe Epidémiologie et Santé Publique, Lyon, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Vanhems
authors.forename: Alain
authors.initials: A
authors.lastname: Barrat
authors.forename: Ciro
authors.initials: C
authors.lastname: Cattuto
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Pinton
authors.forename: Nagham
authors.initials: N
authors.lastname: Khanafer
authors.forename: Corinne
authors.initials: C
authors.lastname: Régis
authors.forename: Byeul-a
authors.initials: BA
authors.lastname: Kim
authors.forename: Brigitte
authors.initials: B
authors.lastname: Comte
authors.forename: Nicolas
authors.initials: N
authors.lastname: Voirin
mesh_terms: D003140:Communicable Disease Control; D003141:Communicable Diseases / Q000175:diagnosis / Q000635:transmission*; D003428:Cross Infection / Q000175:diagnosis / Q000517:prevention & control / Q000635:transmission*; D018562:Disease Transmission, Infectious; D006801:Humans; D008137:Longitudinal Studies; D010362:Patients' Rooms
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