Data

Post COVID-19 data

Dataset	Reference
Epicurus	The role of frailty in shaping social contact patterns in Belgium, 2022–2023
MixIT (Italy)	Implications for distancing measures on in-person school and work attendance from Italian post-pandemic social contact data
Reconnect (UK)	Social contact patterns in the United Kingdom following the COVID-19 pandemic: The Reconnect cross-sectional survey
Social mixing patterns of United States health care personnel at a quaternary health center	Social mixing patterns of United States healthcare personnel at a quaternary health center: a prospective observational study

COVID-19 data

Pre COVID-19 data

Dataset	Reference
POLYMOD	Mossong J, Hens N, Jit M, Beutels P, Auranen K, et al. (2008). Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLOS Medicine 5(3): e74.
Belgium (2006)	Hens N, Goeyvaerts N, Aerts M, Shkedy Z, Van Damme P, Beutels P. (2009). Mining social mixing patterns for infectious disease models based on a two-day population survey in Belgium. BMC Infectious Diseases 9:5.
Belgium (2010-2011)	Willem L, Van Kerckhove K, Chao DL, Hens N, Beutels P. (2012). A nice day for an infection? Weather conditions and social contact patterns relevant to influenza transmission. PloS One 7(11):e48695.
Belgium (2010-2011)	Household members do not contact each other at random: implications for infectious disease modelling
China (Shangai)	Zhang J, Klepac P, Read JM, Rosello A, Wang X, Lai S, Li M, Song Y, Wei Q, Jiang H, et al. (2019). Patterns of human social contact and contact with animals in Shanghai, China. Sci Rep 9(1), 1–11.
France	Béraud G, Kazmercziak S, Beutels P, Levy-Bruhl D, Lenne X, Mielcarek N, et al. (2015). The French Connection: The First Large Population-Based Contact Survey in France Relevant for the Spread of Infectious Diseases. PLoS One 10(7)
Hong Kong	Leung K, Jit M, Lau EHY, Wu JT. (2017). Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Sci Rep 7(1), 1–12
Peru	Grijalva CG, Goeyvaerts N, Verastegui H, Edwards KM, Gil AI, Lanata CF, et al. (2015). A Household-Based Study of Contact Networks Relevant for the Spread of Infectious Diseases in the Highlands of Peru. PLoS One 10(3)
Russia	Litvinova M, Liu QH, Kulikov ES and Ajelli M. (2019). Reactive school closure weakens the network of social interactions and reduces the spread of influenza. Proceedings of the National Academy of Sciences, 116(27), 13174-13181.
Somaliland	van Zandvoort K, Bobe MO, Hassan AI, Abdi MI, Ahmed MS, Soleman SM, Warsame MY, Wais MA, Diggle E, McGowan CR, Satzke C, Mulholland K, Egeh MM, Hassan MM, Hergeeye MA, Eggo RM, Checchi F, Flasche S. Social contacts and other risk factors for respiratory infections among internally displaced people in Somaliland. Epidemics, Volume 41, 2022.
Taiwan	Fu YC, Wang DW, Lin CY, Chuang JH, Yang CH, et al. (2012). Representative Contact Diaries for Modeling the Spread of Infectious Diseases in Taiwan. PLoS One 7(10): e45113.
Thailand (2015)	Mahikul W, Kripattanapong S, Hanvoravongchai P, Meeyai A, et al. (2020). Contact Mixing Patterns and Population Movement among Migrant Workers in an Urban Setting in Thailand. International Journal of Environmental Research and Public Health, 17(7), 2237.
United Kingdom	van Hoek AJ, Andrews N, et al. (2013). The Social Life of Infants in the Context of Infectious Disease Transmission; Social Contacts and Mixing Patterns of the Very Young. PLoS One.
Vietnam	Horby P, Thai PQ, Hens N, Yen NTT, Mai LQ, et al. (2011). Social Contact Patterns in Vietnam and Implications for the Control of Infectious Diseases. PLoS One.
Zambia & South Africa	Dodd PJ, Looker C, Plumb ID, Bond V, et al. (2016). Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection.
Zimbabwe	Melegaro A, Del Fava E, Poletti P, Merler S, Nyamukapa C, et al. (2017). Social Contact Structures and Time Use Patterns in the Manicaland Province of Zimbabwe. PLoS One 12(1)

Each dataset is organised in 6 categories:

Category	Description	Primary key	Foreign key(s)
Participant	Participants information	part_id	hh_id
Contact	Reported contact data, with link to the survey day	part_id	sday_id
Household	Household data	hh_id
Survey day	Information regarding the survey day	sday_id	part_id
Time-use	Information regarding the time use	part_id	sday_id
Dictionary	The dictionary to interpret the columns properly

For most data types, we have two files: one ‘common’ file in which variables are included that are available in most contact surveys; and an ‘extra’ file in which more specific variables related to the survey are included. Merging both files can be done based on the primary key.