Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models.

Van Hoang, T., L. Willem, P. Coletti, K. Van Kerckhove, J. Minnen, P. Beutels, N. Hens (2022): Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models. medRxiv, [Online First], DOI: 10.1101/2022.01.25.22269385, - TOR 2022/8.

Abstract

Background The increasing availability of data on social contact patterns and time use provides invaluable information for studying transmission dynamics of infectious diseases. Social contact data provide information on the interaction of people in a population whereas the value of time use data lies in the quantification of exposure patterns. Both have been used as proxies for transmission risks within in a population and the combination of both sources has led to investigate which kind of social encounters are most relevant to describe transmission risk.

Methods We used social contact and time use data from 1707 participants from a survey conducted in Flanders, Belgium in 2010-2011. We calculated weighted exposure time and social contact matrices to analyze age- and gender-specific mixing patterns and to quantify behavioral changes by distance from home. We compared the value of both data sources, individually and combined, for explaining seroprevalence and incidence data on parvovirus-B19, Varicella-Zoster virus (VZV) and influenza-like illnesses (ILI), respectively.

Results Assortative mixing and inter-generational interaction is more pronounced in the exposure matrix due to the high proportion of time spent at home. This pattern is less pronounced in the social contact matrix, which is more impacted by the reported contacts at school and work. The average number of contacts declined with distance, however on the individual-level, we observed an increase in the number of contacts and the transmission potential by distance when travelling. We found that both social contact data and time use data provide a good match with the seroprevalence and incidence data at hand. When comparing the use of different combinations of both data sources, we found that the social contact matrix based on close contacts of at least 4 hours appeared to be the best proxy for parvovirus-B19 transmission. Social contacts and exposure time were both on their own able to explain VZV seroprevalence data though combining both scored best. Compared with the contact approach, the time use approach provided the better fit to the ILI incidence data.

Conclusions Our work emphasises the common and complementary value of time use and social contact data for analysing mixing behavior and infectious disease transmission. We derived spatial, temporal, age-, gender- and distance-specific mixing patterns, which are informative for future modelling studies.