Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys
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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.
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SciScore for 10.1101/2020.07.12.20151696: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources All study design of The SMART study, including the opt-out consenting process, was approved by the ethics committees at the University of Pittsburgh (PRO1102050), the University of Florida (IRB201701941), the University of Liverpool, and the Centers for Disease Control and Prevention (IRB00000319). SMARTsuggested: (SMART, RRID:SCR_005026)Data availability: De-identified dataset is available through the Zenodo Repository doi:10.5281/zenodo.3940772. Zenodosuggested: (ZENODO, RRID:SCR_004129)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected …SciScore for 10.1101/2020.07.12.20151696: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources All study design of The SMART study, including the opt-out consenting process, was approved by the ethics committees at the University of Pittsburgh (PRO1102050), the University of Florida (IRB201701941), the University of Liverpool, and the Centers for Disease Control and Prevention (IRB00000319). SMARTsuggested: (SMART, RRID:SCR_005026)Data availability: De-identified dataset is available through the Zenodo Repository doi:10.5281/zenodo.3940772. Zenodosuggested: (ZENODO, RRID:SCR_004129)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has some important limitations. Though we adjusted for the demographics of the specific schools and deployments that we conducted, our results may not be generalizable to other settings. The physical and architectural environment of our schools, the density of sensors that we were able to deploy in our schools, and the specific days that we deployed our study may all have affected our results. Technical issues, though not common, did occur with the sensors, resulting in lost data for some sensors. Similarly, recall bias and misclassification by participants when completing contact surveys may have obscured the relationship between our two methodological measurements. We found that the design and administration of contact surveys led to some censoring in the number of contacts reported (Fig. 1). Nonetheless, we believe that the relationships we found were robust to the misclassifications and biases that may be generated by these sources. Previous work has indicated that risk of infection with influenza is more closely linked to the average mixing patterns of an individual’s age group, rather than the individual’s contact behaviour (7). We found that two common methods of collecting social contact data, self-reported surveys and proximity sensors, recorded qualitatively and quantitatively different individual social mixing behaviour but could still generate similar aggregate age-specific social contact patterns. The collection of high-quality social contact data throu...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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