University of Leicester
Browse

Contact tracing is an imperfect tool for controlling COVID-19 transmission and relies on population adherence

Download (1.38 MB)
journal contribution
posted on 2024-07-31, 15:23 authored by Emma L Davis, Tim CD Lucas, Anna Borlase, Timothy M Pollington, Sam Abbott, Diepreye Ayabina, Thomas Crellen, Joel Hellewell, Li Pi, Rachel Lowe, Akira Endo, Nicholas Davies, Georgia R Gore-Langton, Timothy W Russell, Nikos I Bosse, Matthew Quaife, Adam J Kucharski, Emily S Nightingale, Carl AB Pearson, Hamish Gibbs, Kathleen O’Reilly, Thibaut Jombart, Eleanor M Rees, Arminder K Deol, Stéphane Hué, Megan Auzenbergs, Rein MGJ Houben, Sebastian Funk, Yang Li, Fiona Sun, Kiesha Prem, Billy J Quilty, Julian Villabona-Arenas, Rosanna C Barnard, David Hodgson, Anna Foss, Christopher I Jarvis, Sophie R Meakin, Rosalind M Eggo, Kaja Abbas, Kevin van Zandvoort, Jon C Emery, Damien C Tully, Frank G Sandmann, W John Edmunds, Amy Gimma, Gwen Knight, James D Munday, Charlie Diamond, Mark Jit, Quentin Leclerc, Alicia Rosello, Yung-Wai Desmond Chan, David Simons, Sam Clifford, Stefan Flasche, Simon R Procter, Katherine E Atkins, Graham F Medley, T Déirdre Hollingsworth, Petra Klepac

Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate that reporting and adherence are the most important predictors of programme impact but tracing coverage and speed plus diagnostic sensitivity also play an important role. We conclude that well-implemented contact tracing could bring small but potentially important benefits to controlling and preventing outbreaks, providing up to a 15% reduction in R. We reaffirm that contact tracing is not currently appropriate as the sole control measure.

Funding

E.L.D., T.C.D.L., A.B., D.A., L.P., T.M.P., G.F.M. & T.D.H. gratefully acknowledge funding of the NTD Modelling Consortium (NTDMC) by the Bill & Melinda Gates Foundation (BMGF) (grant no. OPP1184344). E.L.D., L.P. & T.D.H. gratefully acknowledge funding from the MRC COVID-19 UKRI/DHSC Rapid Response grant MR/V028618/1 and JUNIPER Consortium (MR/V038613/1). The following funding sources are acknowledged as providing funding for the named authors. This research was partly funded by the Bill & Melinda Gates Foundation (NTDMC: OPP1184344: G.F.M.). This project has received funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (101003688: P.K.). Royal Society (RP/EA/180004: P.K.). Wellcome Trust (210758/Z/18/Z: J.H., S.A.). Views, opinions, assumptions or any other information set out in this article should not be attributed to BMGF or any person connected with them. T.C. is funded by a Sir Henry Wellcome Fellowship from the Wellcome Trust (215919/Z/19/Z). T.M.P.’s PhD is supported by the Engineering & Physical Sciences Research Council, Medical Research Council and University of Warwick (EP/L015374/1) and thanks Big Data Institute for hosting him. All funders had no role in the study design, collection, analysis, interpretation of data, writing of the report, or decision to submit the manuscript for publication

History

Author affiliation

College of Life Sciences, Population Health Sciences

Version

  • VoR (Version of Record)

Published in

Nature Communications

Volume

12

Pagination

5412

Publisher

Springer Science and Business Media LLC

issn

2041-1723

eissn

2041-1723

Acceptance date

2021-08-18

Copyright date

2021

Available date

2024-07-31

Language

en

Deposited by

Dr Tim Lucas

Deposit date

2024-07-25

Rights Retention Statement

  • No

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC