Digital proximity tracing on empirical contact networks for pandemic control
| Autor: | Antonio Longa, Emanuele Pigani, Alain Barrat, Gabriele Santin, Sune Lehmann, Marcel Salathé, Giulia Cencetti, Bruno Lepri, Ciro Cattuto |
|---|---|
| Přispěvatelé: | 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), ANR-19-CE46-0008,DataRedux,Réduction de données massives pour la simulation numérique prédictive(2019) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
2019-20 coronavirus outbreak Isolation (health care) Coronavirus disease 2019 (COVID-19) Epidemiology Computer science Science Control (management) Population Basic Reproduction Number General Physics and Astronomy Tracing General Biochemistry Genetics and Molecular Biology Article 03 medical and health sciences 0302 clinical medicine [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases Models Risk Factors Pandemic Humans Computer Simulation 030212 general & internal medicine [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech] education Pandemics Developing world education.field_of_study Stylized fact Multidisciplinary Models Statistical SARS-CoV-2 Social cost COVID-19 General Chemistry Statistical Health policy 3. Good health University campus Contact Tracing Privacy Quarantine 030104 developmental biology Risk analysis (engineering) Viral infection Basic reproduction number Contact tracing |
| Zdroj: | Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021) Nature Communications Nature Communications, Nature Publishing Group, 2021, 12 (1), ⟨10.1038/s41467-021-21809-w⟩ Nature Communications, 2021, 12 (1), ⟨10.1038/s41467-021-21809-w⟩ |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-021-21809-w⟩ |
| Popis: | Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide. Digital contact tracing is increasingly considered as one of the tools to control infectious disease outbreaks, in particular the COVID-19 epidemic. Here, the authors present a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps. |
| Databáze: | OpenAIRE |
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