Author: Grantz, K. H.; Cummings, D. A. T.; Zimmer, S.; Vukotich, C.; Galloway, D.; Schweizer, M. L.; Guclu, H.; Cousins, J.; Lingle, C.; Yearwood, G. M. H.; Li, K.; Calderone, P. A.; Noble, E.; Gao, H.; Rainey, J.; Uzicanin, A.; Read, J. M.
Title: Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys Cord-id: sj7dd6jr Document date: 2020_7_14
ID: sj7dd6jr
Snippet: 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 correspondenc
Document: 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.
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