IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-37813-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-37813-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-37813-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. William E. Allen & Han Altae-Tran & James Briggs & Xin Jin & Glen McGee & Andy Shi & Rumya Raghavan & Mireille Kamariza & Nicole Nova & Albert Pereta & Chris Danford & Amine Kamel & Patrik Gothe & Evr, 2020. "Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing," Nature Human Behaviour, Nature, vol. 4(9), pages 972-982, September.
    2. Claudia R. Schneider & Sarah Dryhurst & John Kerr & Alexandra L. J. Freeman & Gabriel Recchia & David Spiegelhalter & Sander van der Linden, 2021. "COVID-19 risk perception: a longitudinal analysis of its predictors and associations with health protective behaviours in the United Kingdom," Journal of Risk Research, Taylor & Francis Journals, vol. 24(3-4), pages 294-313, April.
    3. Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-220, April.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cowell, Frank & Flachaire, Emmanuel & Bandyopadhyay, Sanghamitra, 2009. "Goodness-of-fit: an economic approach," LSE Research Online Documents on Economics 25433, London School of Economics and Political Science, LSE Library.
    2. Gerhard Tutz & Moritz Berger, 2018. "Tree-structured modelling of categorical predictors in generalized additive regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 737-758, September.
    3. Bergman, Mats A. & Johansson, Per & Bergman, M.A., 2002. "Large investments in the pulp and paper industry: a count data regression analysis," Journal of Forest Economics, Elsevier, vol. 8(1), pages 29-52.
    4. Tommaso Luzzati & Angela Parenti & Tommaso Rughi, 2017. "Spatial error regressions for testing the Cancer-EKC," Discussion Papers 2017/218, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    5. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    6. Vladimír Hlásny, 2017. "Job applicant screening in China and its four pillars," The Economic and Labour Relations Review, , vol. 28(3), pages 455-473, September.
    7. McMillen, Daniel P. & Smith, Stefani C., 2003. "The number of subcenters in large urban areas," Journal of Urban Economics, Elsevier, vol. 53(3), pages 321-338, May.
    8. Eugenio J. Miravete, 2004. "The Doubtful Profitability of Foggy Pricing," Working Papers 04-07, NET Institute.
    9. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    10. Sihvonen, Markus, 2021. "Yield curve momentum," Research Discussion Papers 15/2021, Bank of Finland.
    11. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    12. Jina Choo & Sooyeon Park & Songwhi Noh, 2021. "Associations of COVID-19 Knowledge and Risk Perception with the Full Adoption of Preventive Behaviors in Seoul," IJERPH, MDPI, vol. 18(22), pages 1-14, November.
    13. Dillon T. Fogarty & Caleb P. Roberts & Daniel R. Uden & Victoria M. Donovan & Craig R. Allen & David E. Naugle & Matthew O. Jones & Brady W. Allred & Dirac Twidwell, 2020. "Woody Plant Encroachment and the Sustainability of Priority Conservation Areas," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    14. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    15. Daniel Melser & Robert J. Hill, 2019. "Residential Real Estate, Risk, Return and Diversification: Some Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 59(1), pages 111-146, July.
    16. Alfonso Gastelum-Strozzi & Claudia Infante-Castañeda & Juan Guillermo Figueroa-Perea & Ingris Peláez-Ballestas, 2021. "Heterogeneity of COVID-19 Risk Perception: A Socio-Mathematical Model," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
    17. Micheels, Eric T. & Nolan, James F., 2016. "Examining the effects of absorptive capacity and social capital on the adoption of agricultural innovations: A Canadian Prairie case study," Agricultural Systems, Elsevier, vol. 145(C), pages 127-138.
    18. Maciej Berȩsewicz & Dagmara Nikulin, 2021. "Estimation of the size of informal employment based on administrative records with non‐ignorable selection mechanism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 667-690, June.
    19. Robert J. Hill & Michael Scholz, 2014. "Incorporating Geospatial Data in House Price Indexes: A Hedonic Imputation Approach with Splines," Graz Economics Papers 2014-05, University of Graz, Department of Economics.
    20. Henderson, Jason R. & McNamara, Kevin T., 2000. "The Location Of Food Manufacturing Plant Investments In Corn Belt Counties," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(2), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37813-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.