Wearable Technologies for Studying Infection Transmission Dynamics in Hospitals

Valeriya Kettelhut, M.D., Ph.D., M.P.H.| UNMC, 2015

Estimating Potential Infection Transmission Routes in Hospital Wards Using

Wearable Proximity SensorsPhilippe Vanhems, Alain Barrat, Ciro Cattuto, Jean-

Francois Pinton, Nagham Khanafer, Corinne Regis, Byeul-a Kim, Brigitte Comte, Nicolas Voirin

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)

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.org

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 PAT RN/Tech NUR

Medical doctor MED Admin 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

ResultsTable 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 individuals

Average number of contacts per individual (SD)

Average duration (seconds) of contacts per individual (SD)

NUR 27 590 (470) 27111 (24395)

PAT 29 136 (112) 6327 (5421)MED 11 558 (341) 27307

(16275)ADM 8 258 (291) 10135

(11439)Overall

75 374 (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-NUR

5,310 (37.8%) 253,900 (39.2%)

NUR–PAT

2,951 (21.0%) 136,900 (21.1%)

MED-MED 2,136 (15.2%) 113,200 (17.5%)NUR–ADM

1,334 (9.5%) 51,920 (8.0%)

MED-NUR 1,021 (7.3%) 35,380 (5.5%)MED-PAT

574 (4.1%) 29,420 (4.5%)

MED-ADM

272 (1.9%) 9,180 (1.4%)

ADM-PAT 227 (1.6%) 8,820 (1.4%)ADM-ADM

115 (0.8%) 5,580 (0.9%)

PAT-PAT 97 (0.7%) 4,180 (0.6%)Total 14,037 (100%) 648,480 (100%)

=180 hNumbers 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

  Number (% of total)

Seconds (% of total)

 Minutes

 Hours

Mornings

9,060 (64.5)

426,860 (65.8)

7,114 118.6

Afternoons

4,165 (29.7)

185,790 (28.7)

3,097 51.6

Days 13,206* (94.1)

612,900 (94.5)

10,215 170.3

Nights 831 (5.9) 35,580 (5.5) 593 9.9

Total 14,037 648,480 10,808 180.1

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.

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.

SUPER-CONTACTORS: SUPER-SPREADERS

6 NUR accounted for 42.1 % of the all contacts

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.

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

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)

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 interactions

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

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

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

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 .

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