For each dataset, you can find DOI’s for both the data repository and the publication reference. Please cite both.
COVID-19 data
Dataset Reference CoMix (Norway) Social contact patterns during the early COVID-19 pandemic in Norway: insights from a panel study, April to September 2020 Contact data from before and after the COVID-19 pandemic in the Netherlands Contact behaviour before, during and after the COVID-19 pandemic in the Netherlands: evidence from contact surveys in 2016-2017 and 2020-2023 Contact data of Children in Belgium, Italy and Poland Contact data of older adults (70+) in the Netherlands in 2021 Backer, J.A., van de Kassteele, J., El Fakiri, F. et al. (2023). Contact patterns of older adults with and without frailty in the Netherlands during the COVID-19 pandemic. BMC Public Health 23 , 1829. CoMix CoMix CoMix CoMix CoMix CoMix CoMix CoMix CoMix CoMix Poland CoMix Portugal CoMix Italy Tizzani, M., De Gaetano, A., Jarvis, C.I. et al. (2023). Impact of tiered measures on social contact and mixing patterns of in Italy during the second wave of COVID-19. BMC Public Health 23 , 906. CoMix France CoMix Spain CoMix Austria CoMix Denmark CoMix UK Gimma A, Munday JD, Wong KLM, Coletti, P et al. (2021). CoMix: Changes in social contacts as measured by the contact survey during the COVID-19 pandemic in England between March 2020 and March 2021 CoMix Netherlands Backer J.A, Bogaardt L. et al (2022): Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands.Scientific Reports 13:5166 CoMix Belgium
Coletti P, Wambua J, Gimma A, Willem L, et al. (2020). CoMix: comparing mixing patterns in the Belgian population during and after lockdown. Scientific Reports. 10:21885
China (Wuhan and Shanghai) Zhang J, Litvinova M, Liang Y, et al. (2020). Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science.
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. Somaliland 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) Peru 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, 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) 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 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 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. 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. 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. China (Shangai) Zhang J., Klepac P., Read J.M., 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 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. 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.
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.