For each dataset, you can find DOI’s for both the data repository and the publication reference.
Please cite both.
Dataset Reference CoMix Greece CoMix Slovenia CoMix Poland CoMix Portugal CoMix Italy 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 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. 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) 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 tuberculosisInfection. 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:
Reported contact data, with link to the survey day
Information regarding the survey day
Information regarding the time use
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.