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Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England

Author

Listed:
  • Younjung Kim

    (University of Sussex)

  • Christl A. Donnelly

    (University of Oxford
    University of Oxford
    Imperial College London)

  • Pierre Nouvellet

    (University of Sussex
    Imperial College London)

Abstract

During the COVID-19 pandemic, national testing programmes were conducted worldwide on unprecedented scales. While testing behaviour is generally recognised as dynamic and complex, current literature demonstrating and quantifying such relationships is scarce, despite its importance for infectious disease surveillance and control. Here, we characterise the impacts of SARS-CoV-2 transmission, disease susceptibility/severity, risk perception, and public health measures on SARS-CoV-2 PCR testing behaviour in England over 20 months of the pandemic, by linking testing trends to underlying epidemic trends and contextual meta-data within a systematic conceptual framework. The best-fitting model describing SARS-CoV-2 PCR testing behaviour explained close to 80% of the total deviance in NHS test data. Testing behaviour showed complex associations with factors reflecting transmission level, disease susceptibility/severity (e.g. age, dominant variant, and vaccination), public health measures (e.g. testing strategies and lockdown), and associated changes in risk perception, varying throughout the pandemic and differing between infected and non-infected people.

Suggested Citation

  • Younjung Kim & Christl A. Donnelly & Pierre Nouvellet, 2023. "Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37813-1
    DOI: 10.1038/s41467-023-37813-1
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    References listed on IDEAS

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    4. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
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