Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys

Abstract

Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.

On the parameterization of mathematical models of infectious disease transmission structured by age at the start of the epidemic spread

“…The main problem with the information extracted from conversational surveys lies in the definition of contact, which can have a different impact for different diseases, and in the need to do them at a certain time and place, given that their extrapolation to other social conditions is not obvious. In any case, given that none approach is entirely conclusive because of the complexity of the problem, a sensible practice (when possible) is to use several complementary approaches to confirm the predictions of the models [16][17][18][19].…”

Colonization and transmission of Staphylococcus aureus in schools: a citizen science project

“…As a consequence, there has been considerable interest in the use of social contact networks to support or strengthen inference from sequence data [ 19 ]. Outside of infections with highly specific routes of transmission, such as sexually transmitted diseases, comparatively less attention has been paid to identifying the types of social interaction most closely correlated to transmission of communicable infections [ 19 ] or specifically comparing the contact patterns within schools with transmission [ 20 ]. To our knowledge, no study has previously attempted to use microbial genomics to track transmission networks within a school environment and compare with social networks, though in the recent COVID-19 pandemic this has been done in universities [ 21 ].…”