Close Encounters in a Pediatric Ward: Measuring Face-to-Face Proximity and Mixing Patterns with Wearable Sensors

Abstract

BackgroundNosocomial infections place a substantial burden on health care systems and represent one of the major issues in current public health, requiring notable efforts for its prevention. Understanding the dynamics of infection transmission in a hospital setting is essential for tailoring interventions and predicting the spread among individuals. Mathematical models need to be informed with accurate data on contacts among individuals.Methods and FindingsWe used wearable active Radio-Frequency Identification Devices (RFID) to detect face-to-face contacts among individuals with a spatial resolution of about 1.5 meters, and a time resolution of 20 seconds. The study was conducted in a general pediatrics hospital ward, during a one-week period, and included 119 participants, with 51 health care workers, 37 patients, and 31 caregivers. Nearly 16,000 contacts were recorded during the study period, with a median of approximately 20 contacts per participants per day. Overall, 25% of the contacts involved a ward assistant, 23% a nurse, 22% a patient, 22% a caregiver, and 8% a physician. The majority of contacts were of brief duration, but long and frequent contacts especially between patients and caregivers were also found. In the setting under study, caregivers do not represent a significant potential for infection spread to a large number of individuals, as their interactions mainly involve the corresponding patient. Nurses would deserve priority in prevention strategies due to their central role in the potential propagation paths of infections.ConclusionsOur study shows the feasibility of accurate and reproducible measures of the pattern of contacts in a hospital setting. The obtained results are particularly useful for the study of the spread of respiratory infections, for monitoring critical patterns, and for setting up tailored prevention strategies. Proximity-sensing technology should be considered as a valuable tool for measuring such patterns and evaluating nosocomial prevention strategies in specific settings.

Temporal networks

“…Here, the pioneering study was the Reality Mining project, where students of Massachusetts Institute of Technology were equipped with cell phones whose Bluetooth devices could detect their proximity to others [37]. The SocioPatterns project has developed a platform that allows physical proximity measurements based on wearable badges equipped with radiofrequency identification devices (RFID) [24]; these devices have been utilized in measurements of dynamic and temporal proximity networks of patients [63], school children [142], and conference attendees [140]. Because the human body acts as a shield for the proximity-sensing RF signals, such sensors only record contacts when the individuals are facing each other, and thus a contact can also be considered as indicative of communication between the individuals [24,64,121,141].…”

Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles

“…They analyze, for example, their activity in social web platforms like Facebook, Twitter and other social media together with status and their research seniority. These experiments have also extended their focus from conferences to schools [10] and hospitals [11].…”

Predictability of Conversation Partners

“…The interval between successive conversation events for an individual or a given pair of individuals often follows a power law [1,[4][5][6]8,[17][18][19][20]. Modeling studies have revealed implications of these empirical results in contagions [4,6,9,[21][22][23][24][25] and opinion formation [26,27]. In contrast to conventional models in which the Poisson interval distribution is assumed, these results indicate that the next conversation time, given the previous one, is relatively predictable in that a conversation event in the recent past is a precursor to a burst of events in the near future.…”