Engagement and adherence trade-offs for SARS-CoV-2 contact tracing.


Journal

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
ISSN: 1471-2970
Titre abrégé: Philos Trans R Soc Lond B Biol Sci
Pays: England
ID NLM: 7503623

Informations de publication

Date de publication:
19 07 2021
Historique:
entrez: 31 5 2021
pubmed: 1 6 2021
medline: 11 6 2021
Statut: ppublish

Résumé

Contact tracing is an important tool for allowing countries to ease lockdown policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuading individuals from self-reporting. We use an existing branching process model to examine which aspects of contact tracing adherence should be prioritized. We consider an inverse relationship between self-isolation adherence and self-reporting engagement, assuming that increasingly strict self-isolation policies will result in fewer individuals self-reporting to the programme. We find that policies which increase the average duration of self-isolation, or that increase the probability that people self-isolate at all, at the expense of reduced self-reporting rate, will not decrease the risk of a large outbreak and may increase the risk, depending on the strength of the trade-off. These results suggest that policies to increase self-isolation adherence should be implemented carefully. Policies that increase self-isolation adherence at the cost of self-reporting rates should be avoided. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Identifiants

pubmed: 34053257
doi: 10.1098/rstb.2020.0270
pmc: PMC8165588
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

20200270

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V038613/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 215919/Z/19/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 210758/Z/18/Z
Pays : United Kingdom

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Auteurs

Tim C D Lucas (TCD)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Emma L Davis (EL)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Diepreye Ayabina (D)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Anna Borlase (A)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Thomas Crellen (T)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Li Pi (L)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Graham F Medley (GF)

MathSys CDT, University of Warwick, Coventry, UK.
Centre for Mathematical Modelling of Infectious Disease and Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.

Lucy Yardley (L)

School of Psychology, University of Southampton, Southampton, UK.
School of Psychological Science, University of Bristol, Bristol, UK.

Petra Klepac (P)

Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Department for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Julia Gog (J)

Department for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

T Déirdre Hollingsworth (T)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

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