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Epidemic mitigation by statistical inference from contact tracing data

Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical r...

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Autores principales: Baker, Antoine, Biazzo, Indaco, Braunstein, Alfredo, Catania, Giovanni, Dall’Asta, Luca, Ingrosso, Alessandro, Krzakala, Florent, Mazza, Fabio, Mézard, Marc, Muntoni, Anna Paola, Refinetti, Maria, Sarao Mannelli, Stefano, Zdeborová, Lenka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364197/
https://www.ncbi.nlm.nih.gov/pubmed/34312253
http://dx.doi.org/10.1073/pnas.2106548118
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author Baker, Antoine
Biazzo, Indaco
Braunstein, Alfredo
Catania, Giovanni
Dall’Asta, Luca
Ingrosso, Alessandro
Krzakala, Florent
Mazza, Fabio
Mézard, Marc
Muntoni, Anna Paola
Refinetti, Maria
Sarao Mannelli, Stefano
Zdeborová, Lenka
author_facet Baker, Antoine
Biazzo, Indaco
Braunstein, Alfredo
Catania, Giovanni
Dall’Asta, Luca
Ingrosso, Alessandro
Krzakala, Florent
Mazza, Fabio
Mézard, Marc
Muntoni, Anna Paola
Refinetti, Maria
Sarao Mannelli, Stefano
Zdeborová, Lenka
author_sort Baker, Antoine
collection PubMed
description Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible but before the fraction of infected people reaches the scale where a lockdown becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized, and thus, it is compatible with privacy-preserving standards. We conclude that probabilistic risk estimation is capable of enhancing the performance of digital contact tracing and should be considered in the mobile applications.
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spelling pubmed-83641972021-08-24 Epidemic mitigation by statistical inference from contact tracing data Baker, Antoine Biazzo, Indaco Braunstein, Alfredo Catania, Giovanni Dall’Asta, Luca Ingrosso, Alessandro Krzakala, Florent Mazza, Fabio Mézard, Marc Muntoni, Anna Paola Refinetti, Maria Sarao Mannelli, Stefano Zdeborová, Lenka Proc Natl Acad Sci U S A Physical Sciences Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible but before the fraction of infected people reaches the scale where a lockdown becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized, and thus, it is compatible with privacy-preserving standards. We conclude that probabilistic risk estimation is capable of enhancing the performance of digital contact tracing and should be considered in the mobile applications. National Academy of Sciences 2021-08-10 2021-07-26 /pmc/articles/PMC8364197/ /pubmed/34312253 http://dx.doi.org/10.1073/pnas.2106548118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Baker, Antoine
Biazzo, Indaco
Braunstein, Alfredo
Catania, Giovanni
Dall’Asta, Luca
Ingrosso, Alessandro
Krzakala, Florent
Mazza, Fabio
Mézard, Marc
Muntoni, Anna Paola
Refinetti, Maria
Sarao Mannelli, Stefano
Zdeborová, Lenka
Epidemic mitigation by statistical inference from contact tracing data
title Epidemic mitigation by statistical inference from contact tracing data
title_full Epidemic mitigation by statistical inference from contact tracing data
title_fullStr Epidemic mitigation by statistical inference from contact tracing data
title_full_unstemmed Epidemic mitigation by statistical inference from contact tracing data
title_short Epidemic mitigation by statistical inference from contact tracing data
title_sort epidemic mitigation by statistical inference from contact tracing data
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364197/
https://www.ncbi.nlm.nih.gov/pubmed/34312253
http://dx.doi.org/10.1073/pnas.2106548118
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