Towards pandemic preparedness: ability to estimate high-resolution social contact patterns from longitudinal surveys
Shozen Dan, Joshua Tegegne, Yu Chen, Zhi Ling, Veronika K. Jaeger,, Andr\'e Karch, Swapnil Mishra, and Oliver Ratmann (on behalf of the Machine, Learning, Global Health network)

TL;DR
This study shows that accounting for reporting fatigue in longitudinal social contact surveys improves the accuracy of infection risk estimates, supporting their use in pandemic preparedness.
Contribution
We demonstrate a simple statistical model to correct for reporting fatigue in longitudinal social contact surveys, enhancing their reliability for infectious disease modeling.
Findings
Reporting fatigue varies by sociodemographic factors.
Statistical adjustments improve estimation accuracy.
Longitudinal surveys with repeat participants are viable.
Abstract
Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by reporting fatigue, a phenomenon whereby the average number of social contacts reported declines with the number of repeat participations and as participants' engagement decreases over time. Using data from the German COVIMOD Study between April 2020 to December 2021, we demonstrate that reporting fatigue varied considerably by sociodemographic factors and was consistently strongest among parents reporting children contacts (parental proxy reporting), students, middle-aged individuals, those in full-time employment and those self-employed. We find further…
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Taxonomy
TopicsCOVID-19 epidemiological studies
