Pierre-Yves Boëlle, Alain Barrat, Francesco Pinotti, Chiara Poletto, Livio Bioglio, Beatriz Arregui García, Jesús A. Moreno López, Vittoria Colizza, Piotr Bentkowski, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Institute for Cross-Disciplinary Physics and Complex Systems [Mallorca] (IFISC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universitat de les Illes Balears (UIB), University of Turin, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Agence Nationale de la Recherche (France), European Commission, Fondation de France, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Poletto, Chiara [0000-0002-4051-1716], Poletto, Chiara, Università degli studi di Torino = University of Turin (UNITO), ANR-19-CE46-0008,DataRedux,Réduction de données massives pour la simulation numérique prédictive(2019), and ANR-20-COVI-0070,NoCOV,Prévisions au court et moyen terme de la diffusion de COVID-19 dans la population générale française(2020)
The efficacy of digital contact tracing against COVID-19 epidemic is debated: smartphone penetration is limited in many countries, non-uniform across age groups, with low coverage among elderly, the most vulnerable to SARS-CoV-2. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact-survey and epidemiological information to describe the risk factors for exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan., This study was partially supported by the ANR project DATAREDUX (ANR-19-CE46-0008-01) to AB, VC and PYB; EU H2020 grants MOOD (H2020-874850) to VC, CP, and PYB, and RECOVER (H2020-101003589) to VC; the Municipality of Paris (https://www.paris.fr/) through the programme Emergence(s) to JAML, BAG, PB, FP and CP; the ANR and Fondation de France through the project NoCOV (00105995) to PYB, CP; the Spanish Ministry of Science and Innovation to JAML and BAG, the AEI and FEDER (EU) under the grant PACSS (RTI2018-093732-B-C22) JAML and BAG; the Maria de Maeztu program for Units of Excellence in R&D (MDM-2017-0711) to JAML and BAG.