Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness

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 survey 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 survey 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 that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for survey fatigue were associated with substantially improved estimation accuracy relative to models with no survey fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach addressing survey fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs.

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Publication Details

Journal
Scientific Reports
Published
2025-05-23
DOI
https://doi.org/10.1038/s41598-025-02235-0
Citations
1
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
1.37

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article

Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness

Shozen Dan, Swapnil Mishra, Oliver Ratmann, Zhiyu Ling et al.
1 citations
Scientific Reports
COVID-19 epidemiological studies
1.37
article

Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness

Shozen Dan, Swapnil Mishra, Oliver Ratmann, Zhiyu Ling, André Karch, Veronika K. Jaeger, Yu Chen, Joshua Tegegne
article en
1 citations

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 survey 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 survey 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 that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for survey fatigue were associated with substantially improved estimation accuracy relative to models with no survey fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach addressing survey fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs.

Scientific ReportsVol. 15(1)
University of Copenhagen (DK), Data Storage Institute (SG), University of Kaiserslautern (DE), National University of Singapore (SG), University of Münster (DE), German Research Centre for Artificial Intelligence (DE), University of Cyprus (CY), University of Bristol (GB), University of Warwick (GB), University of Oxford (GB), Science Oxford (GB), National University Health System (SG), Imperial College London (GB), The University of Adelaide (AU)
Bill and Melinda Gates Foundation, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Moderna, Imperial College London, National Research Foundation, National Research Foundation Singapore, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Westfälische Wilhelms-Universität Münster, Robert Koch Institut, Medical Research Council, Engineering and Physical Sciences Research Council, National Medical Research Council
Good health and well-being
Openalex Percentile: Top 21%
COVID-19 epidemiological studies
1.37
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