Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

Almagor, J. and Picascia, S. (2020) Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model. Scientific Reports, 10, 22235. (doi: 10.1038/s41598-020-79000-y) (PMID:33335125) (PMCID:PMC7746740)

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Abstract

A contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Picascia, Dr Stefano and Almagor, Dr Jonatan
Authors: Almagor, J., and Picascia, S.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > MRC/CSO SPHSU
Journal Name:Scientific Reports
Publisher:Nature Research
ISSN:2045-2322
ISSN (Online):2045-2322
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Scientific Reports 10: 22235
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
727621Neighbourhoods and CommunitiesAnne EllawayMedical Research Council (MRC)MC_UU_12017/10HW - MRC/CSO Social and Public Health Sciences Unit
727661Complexity in Health ImprovementLaurence MooreMedical Research Council (MRC)MC_UU_12017/14HW - MRC/CSO Social and Public Health Sciences Unit
727621Neighbourhoods and CommunitiesAnne EllawayOffice of the Chief Scientific Adviser (CSO)SPHSU10HW - MRC/CSO Social and Public Health Sciences Unit