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CoMix social contact data (Italy)

CoMix social contact data for Italy. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Daniela Paolotti at the ISI Foundation.

CoMix social contact data (Denmark)

CoMix social contact data for Austria. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Michael Bang Petersen at Aarhus University.

opencc-by-4.0Jul 2021View details →

CoMix social contact data (Greece)

CoMix social contact data for Greece. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Elpida Pavi at the University of West Attica (UniWA).

CoMix social contact data (Portugal)

CoMix social contact data for Portugal. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Baltazar Nunes at the National Health Institute Doutor Ricardo Jorge (INSA).

CoMix social contact data (Poland)

CoMix social contact data for Poland. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Magdalena Rosinska at the National Institute of Public Health - National Institute of Hygiene.

CoMix social contact data (France)

CoMix social contact data for France. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Guillaume Béraud at the Centre Hospitalier Universitaire de Poitiers.

opencc-by-4.0Sep 2022View details →

Social contact data for IDPs in Somaliland (2019)

Social contact data for internally displaced people (IDP) living in Digaale IDP camp in Somaliland. Participants reported all their direct contacts in the 24 hours preceding the survey. This survey was conducted in 2019. See the corresponding paper for more information: <a href="https://doi.org/10.1016/j.epidem.2022.100625">https://doi.org/10.1016/j.epidem.2022.100625</a> Data is formatted to be used in the socialmixr package in R.

opencc-by-4.0Sep 2022View details →

Nationally Representative Social Contact Patterns among U.S. adults, August 2020-September 2021

The CovidVu study is a national probability survey that collected data on SARS-CoV-2 infection in the U.S. in three rounds: August-December 2020, March-April 2021, and July-September 2021. The goal of this study was to estimate cumulative incidence of SARS-CoV-2 infection in the United States. Surveys were mailed to randomly sampled households in the U.S., and one adult member of the household filled out the questionnaire and sent back SARS-CoV-2 specimens for testing. The data collected included information on social contacts by age, location, and whether they were physical or nonphysical contacts, as well as key demographic information on participants. Survey weights were used to determine unbiased estimates of key parameters, and were based on the population of noninstitutionalized, housed adults (>=18 years of age) in the U.S. Weights were calculated with respect to gender, age, race/ethnicity, education, income, marital status, and census division. For more in-depth information, please visit https://prismhealth.emory.edu/covidvu/ to view recent publications using this data.

opencc-by-4.0May 2021View details →

CoMix social contact data (Netherlands)

Social contact data for Netherlands from the CoMix survey. Changelog: Version 2: Updated time frame.

opencc-by-4.0Oct 2020View details →

Social contact data before and during COVID-19 in China

Social contact data for Wuhan and Shanghai, China before and during the COVID-19 outbreak. Changelog for Version 2: - Contact data from outbreak and baseline merged. A variable to distinguish the two ("collection_period") is added to "contact_extra". For problems with the dataset, please contact: <table> <tbody> <tr> <td> </td> <td>socialcontactdata@gmail.com</td> </tr> </tbody> </table>

opencc-by-4.0Feb 2023View details →

Social contact patterns relevant for infectious disease transmission in Cambodia

Social contact data from a community-based survey conducted in Cambodia in 2012.

opencc-by-4.0Dec 2022View details →

Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021

These are data from the CorporateMix US study rounds 1-4. Below is a description of the files: 1. participants: this contains a list of all the study participants for each round. EaA unique participant  identified by the participant_id and round. 2. contacts: this contains the individuals with whom a participant had a contact. 3.df_all: this is generated by merging the participant and contacts dataframes using the participant_id and round as primary keys. 4. day_rd1: this is data for round 1 day of survey. The survey design for round 1 was different, so these data are important to distinguish between day 1 and day 2 contacts.

opencc-by-4.0Aug 2019View details →

Social contact data for China mainland

Social contact data for China mainland

opencc-by-4.0Jul 2020View details →

SMART2: Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys

To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area.   Enrollment in the Social Mixing and Respiratory Transmission (SMART) study operated on an opt-out basis, and all students registered in a participating school before the start of the study were eligible to participate. Students in kindergarten (typically aged 5 years) to 12th grade (typically aged 18 years) from two elementary (K to 4th grade, K to 5th grade), two middle (5th to 6th grade, 7th to 8th grade), two elementary-middle (K to 8th grade), and two high (both 9th to 12th grade) schools were eligible to participate in SMART. Participation rates were high in all schools (82 to 99%). Each school provided aggregate demographic information about the school population, and individual grade and sex of participating students. Proximity sensor deployments The details of proximity sensor deployments have been described in detail elsewhere (60). In brief, participating students were given proximity sensors in plastic pouches and instructed to wear the pouch around their neck for the duration of the school day without removing or otherwise tampering with the sensor. In six of the eight schools, all participating students were given a sensor; in two schools, the large student population limited the deployment to randomly selected classrooms in each grade. Deployments typically lasted from the first class period (08:00 – 09:00) to the last class period (14:00 – 15:00). Deployment days in each school were chosen to be representative of a typical school day, without any special schoolwide or grade-specific activities that could modify normal contact patterns.   We used TelosB wireless sensors (61) programmed in the NesC language to send beacons every 20 seconds (beacon frequency 3 per minute). The receiving sensor recorded the contacting sensor’s identity, an internal time stamp, and a radio strength signal indicator (RSSI). Signal strength provided an estimate of physical proximity, but was highly dependent on the orientation of the two sensors and any obstructions between them and therefore could not be used to define an exact distance between contacts. Based on pilot studies and previous work on effective distances of respiratory virus transmission (29, 62), we chose a signal threshold (-80 dBm) that should correspond to contacts of relevance to respiratory disease transmission.   The number of unique proximity sensor contacts recorded for a participant was defined as the total number of other participants with whom their proximity sensor recorded at least one interaction during each deployment. To explore patterns of contacts of varying length, we considered several values of the contact threshold, or the minimum number of recorded interactions between two proximity sensors required to be considered a unique contact. The number of interactions between any given pair of sensors was taken to be the maximum number of interactions recorded by either sensor, to account for battery failure, measurement error, or other malfunctions.   Contact survey design Contact surveys were completed by participants in school under the supervision of project staff and teachers. Each sheet of the paper version allowed for information on up to 30 contacts to be recorded; additional sheets could be requested. Two versions were designed: one for middle- and high-school students, and a simplified version for elementary school students (although some elementary school children completed the middle- and high-school version, upon consultation with school administrators and teachers). Classrooms were randomly selected to participate from each grade, and students of several classrooms completed more than one contact survey over the course of the study period.   Participants were asked to report information about any individual they talked with, played with, or touched the previous day, including the contact’s age and sex, whether they attended the same school as the participant, the context in which the contact was made, whether the contact involved direct or indirect (through a shared object) touch, and approximate duration of the contact. Students reported the total number of contacts made in the previous day, without detailed information, and additional demographic information about themselves and their household. The surveys were completed either on paper or by computer, depending on resources available in each school.   We defined total survey contacts as the total number of individuals a student reported having interacted with on the day before the survey was completed. Detailed contacts were the subset of total contacts for which the student reported contact age, sex, duration, and context. We considered further subsets of detailed survey contacts, including those occurring within school, those reported to have lasted more than 10 minutes over the course of the day, and those occurring on the same day as a sensor deployment.

opencc-by-4.0Dec 2019View details →

Social contact data for Belgium (2006)

Social contact data from a two-day population survey in Belgium in 2006

opencc-by-4.0Apr 2022View details →

Social contact data for Taiwan

Social contact data for Taiwan, based on a national survey conducted in 2010

opencc-by-4.0May 2022View details →

CoMix social contact data (Lithuania)

CoMix social contact data for Lithuania. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Marija Jakubauskiene at Vilnius University.

opencc-by-4.0May 2022View details →

CoMix social contact data (Finland)

CoMix social contact data for Finland. We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Kari Auranen at the Finnish Institute for Health and Welfare (THL).