Evidence map›Paper›PMID 40410335›Full record

ArticleScientific reports2025

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

Shozen Dan, Zhi Ling, Yu Chen, Joshua Tegegne, Veronika K Jaeger, André Karch, Swapnil Mishra, Oliver Ratmann, Machine Learning &Global Health network

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Shozen Dan *Department of Mathematics, Imperial College London, London, UK. shozen.dan21@imperial.ac.uk.
Zhi LingDepartment of Mathematics, Imperial College London, London, UK.
Yu ChenDepartment of Mathematics, Imperial College London, London, UK.
Joshua TegegneDepartment of Mathematics, Imperial College London, London, UK.
Veronika K JaegerUniversity of Münster, Institute of Epidemiology and Social Medicine, Münster, Germany.
André KarchUniversity of Münster, Institute of Epidemiology and Social Medicine, Münster, Germany.
Swapnil MishraNational University of Singapore, Saw Swee Hock School of Public Health, Singapore, Singapore.
Oliver Ratmann *Department of Mathematics, Imperial College London, London, UK. oliver.ratmann@imperial.ac.uk.
Machine Learning &Global Health network

Funding

Long-term impact of universal treatment and dolutegravir on population HIV virologic and incidence outcomes in Africa: The LONGVIEW StudyR01AI155080 · NIAID · JOHNS HOPKINS UNIVERSITY · PI GRABOWSKI, MARY KATHRYN · 2021 to 2024
$2.5M
Bill and Melinda Gates Foundation OPP1175094Engineering and Physical Sciences Research Council EP/X038440/1EPSRC Centre for Doctoral Training in Modern Statistics and Machine Learning at Imperial and Oxford EP/S023151/1National Institute of Health, United Kingdom R01AI155080National Medical Research Council, Singapore Ministry of Health PREPARE-S2-2024-002National Research Foundation Singapore NRF-NRFF15-2023-0010NIAID NIH HHS R01 AI155080
6 · The paper itself

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.

Indexed as

COVID-19FatigueAdolescentAdultBiasFemaleGermanyHumansLongitudinal StudiesMaleMiddle AgedPandemic PreparednessPandemicsSARS-CoV-2Social BehaviorSurveys and Questionnaires

Identifiers

PMID40410335
PMCPMC12102313

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.