1. Wearable Technologies for Studying Infection Transmission Dynamics in Hospitals Valeriya Kettelhut, M.D., Ph.D., M.P.H.| UNMC, 2015

2. Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors Philippe Vanhems, Alain Barrat, Ciro Cattuto, Jean-Francois Pinton, Nagham Khanafer, Corinne Regis, Byeul-a Kim, Brigitte Comte, Nicolas Voirin

3. Data on Infection Transmissions in Hospitals  Close-range contacts are strong determinants of potential transmissions of infectious agents  The accurate description of contact patterns between individuals is critical  for better understanding of the possible transmission dynamics  for designing better infection prevention and control measures  Problem: acquisition of reliable data on these behaviors  Current methods of gathering data  Surveys  Diaries  Time use records  Problems with these methods  Lack of longitudinal dimension  Lack of high spatial and temporal resolution (distance and time spent for a contact )

4. Methods for Contact Data Collection  Sensor-based data collection  Wearable badge with ultra-low power radio packets: small active RFID devices  The study system was tuned to a specific distance 1.5 m when the radio packets exchange can occur  Condition: the probability to detect this distance over a time interval of 20 sec. should be larger than 90%  Location of the sensor: the chest Position: face-to-face  The signals are detected by the sensor and sent to a radio receiver  Definition of “contact”: two individual are in “contact” when their sensors exchanged at least one packet during 20 sec  COMMENT: physical contact vs. being in proximity  The SocioPatterns collaboration: dataset www.sociopatterns.orgwww.sociopatterns.org

5. Study Setting, Design, and Data Collection  Acute geriatric unit of a university: 19 beds  Contact event: close-range interactions  Subjects  29 Patients (Pt-HCW) 94% p.rate  46 Healthcare workers (HCW-HCW) 92% Nurses, nutritionist, physiotherapist, physicians, interns  5 daytime periods and 4 night periods (Monday at 1:00 pm to Friday at 2:00 pm)  Patient data were de-identified  Individuals categorized by “role”  Patients PATRN/Tech NUR  Medical doctor MEDAdmin ADM  Measurements for each individual (contact matrices) 1. Number of distinct contacts per each individual 2. Total number of contacts for each individual 3. Duration of each contact for each individual

6. Results Table 1. Number of individuals in each class, and average number and duration of contacts during the study per individual in each class. Group* Number of individua ls Average number of contacts per individual (SD) Average duration (seconds) of contacts per individual (SD) NUR27590 (470)27111 (24395) PAT29136 (112)6327 (5421) MED11558 (341)27307 (16275) ADM8258 (291)10135 (11439) Overall75374 (390)17293 (19265) Numbers in parenthesis give the standard deviation. *Abbreviations: NUR, paramedical staff (nurses and nurses’ aides); PAT, Patient; MED, Medical doctor; ADM, administrative staff. doi:10.1371/journal.pone.0073970.t001 Table 2. Total number and duration of contacts between pairs of individuals belonging to specific classes. Pair*Contact number Cumulative duration (sec) NUR-NUR5,310 (37.8%)253,900 (39.2%) NUR–PAT2,951 (21.0%)136,900 (21.1%) MED-MED2,136 (15.2%)113,200 (17.5%) NUR–ADM1,334 (9.5%)51,920 (8.0%) MED-NUR1,021 (7.3%)35,380 (5.5%) MED-PAT574 (4.1%)29,420 (4.5%) MED-ADM272 (1.9%)9,180 (1.4%) ADM-PAT227 (1.6%)8,820 (1.4%) ADM-ADM115 (0.8%)5,580 (0.9%) PAT-PAT97 (0.7%)4,180 (0.6%) Total14,037 (100%)648,480 (100%) =180 h Numbers in parenthesis give the percentage with respect to the total number and durations of all detected contacts. *Abbreviations: NUR, paramedical staff (nurses and nurses’ aides); PAT, Patient; MED, Medical doctor; ADM, administrative staff. doi:10.1371/journal.pone.0073970.t002

7. Number (% of total) Seconds (% of total) Minutes Hours Mornings9,060 (64.5)426,860 (65.8)7,114118.6 Afternoons4,165 (29.7)185,790 (28.7)3,09751.6 Days13,206* (94.1)612,900 (94.5)10,215170.3 Nights831 (5.9)35,580 (5.5)5939.9 Total14,037648,48010,808180.1 Table 3. Number and duration of contacts between individuals in the various periods of the days, aggregated over the observation period of 4 workdays and 4 nights Figure 3. Contacts matrices between classes of individuals in each morning, afternoon and night. In each matrix, the entry at row X and column Y gives the total number of contacts of all individuals of class X with all individuals of class Y during each period. Abbreviations: NUR, paramedical staff (nurses and nurses’ aides); PAT, Patient; MED, Medical doctor; ADM, administrative staff.

8. Figure 2. Number of contacts per 1-hour periods. The evolution of the number of contacts at the more detailed resolution of one-hour time windows is reported in Figure 2. The number of contacts varied strongly over the course of a day, but the evolution was similar from one day to another (for day 1 and day 5, contacts were recorded after 1:00 pm and before 2:00 pm respectively, with very few contacts at night and a maximum around 10–12 am.

9. SUPER-CONTACTORS: SUPER-SPREADERS 6 NUR accounted for 42.1 % of the all contacts

10. Conclusions and Future Work  Data can be used to explore the spread of infection through mathematical and computational modeling  data can help to accurately inform computational models of the propagation of infectious diseases and, as a consequence, to improve the design and implementation of prevention or control measures based on the frequency and duration of contacts  The possibility for HCWs to be super-contactors emphasizes the need to reduce their exposure to infection and to limit the risk of transmission to patients.  HCWs could be warned against the risk brought forth by unnecessary large numbers or long durations of contacts, especially with patients.

11. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices Anna Machens, Francesco Gesualdo, Caterina Rizzo, Alberto E Tozzi, Alain Barrat and Ciro Cattuto

12. Study Aim  to compare different numerical simulations of the spread of an infectious disease, where each simulation is constructed on top of a specific mathematical representation of contact patterns, and all these representations are derived from the same empirical data, summarized or modeled at different levels of detail (e.g., individual-based contact network vs contact matrices)

13. Study Setting, Design, and Data Collection  The Department of Pediatrics  It has 44 beds arranged in 22 rooms with 2 beds  Children with acute diseases who do not require intensive care or surgery  The pandemic period when several patients with H1N1 infection were admitted  Contact event: close-range interaction s  119 Individuals categorized by “role”  37 patients (P), 20 physicians (D), 21 nurses (N), 10 ward assistants (A), 31 caregivers (C)  One week for data collection

14. Background  The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts.  The integration of highly detailed data sources yields models that are less transparent and general in their applicability.  Given a specific disease model (SEIR), it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail  SEIR model: the susceptible, exposed, infectious, recovered model

15. Method  Type of data: high-resolution data on the face-to-face interactions of individuals in a pediatric hospital ward, obtained by using wearable proximity sensors  To simulate the spread of a disease in this ped. community, an SEIR model (with births, deaths, or introduction of individuals) was used on top of different mathematical representations of the empirical contact patterns  All contacts between individuals and their exact timing and order were taken into account  A hierarchy of coarse-grained representations of the contact patterns was built  The dynamics of the SEIR model were compared across these representations

16. Findings  A contact matrix that only contains average contact durations between role classes fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes.  The investigators introduced a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and showed that this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data.  The role class of the initial seed has a strong impact on the extinction probability and on the probability of observing a large outbreak: if the seed is a ward assistant or a nurse, the probability of a large outbreak is much larger.  In addition, assistants and nurses have an overall larger risk compared to the other role classes. These results are consistent with literature that highlights the crucial importance of prioritizing nurses for local infection control interventions.

17. Conclusions  The results mark a first step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies