For each dataset, you can find DOI’s for both the data repository and the publication reference. Please cite both.

COVID-19 data

Pre COVID-19 data

POLYMODMossong 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.
PeruGrijalva 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)
ZimbabweMelegaro 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)
FranceBéraud 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 KongLeung 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
VietnamHorby 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 Kingdomvan 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 AfricaDodd PJ, Looker C, Plumb ID, Bond V, et al. (2016). Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosisInfection.
RussiaLitvinova 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.