Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment

Abstract Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in li...

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Autores principales:	Alberto Ferrari, Enrico Santus, Davide Cirillo, Miguel Ponce-de-Leon, Nicola Marino, Maria Teresa Ferretti, Antonella Santuccione Chadha, Nikolaos Mavridis, Alfonso Valencia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/1dd0165ab9d2495b8cccf21a610df530
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spelling	oai:doaj.org-article:1dd0165ab9d2495b8cccf21a610df5302021-12-02T15:59:41ZSimulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment10.1038/s41746-020-00374-42398-6352https://doaj.org/article/1dd0165ab9d2495b8cccf21a610df5302021-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00374-4https://doaj.org/toc/2398-6352Abstract Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded S I R model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies.Alberto FerrariEnrico SantusDavide CirilloMiguel Ponce-de-LeonNicola MarinoMaria Teresa FerrettiAntonella Santuccione ChadhaNikolaos MavridisAlfonso ValenciaNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Alberto Ferrari Enrico Santus Davide Cirillo Miguel Ponce-de-Leon Nicola Marino Maria Teresa Ferretti Antonella Santuccione Chadha Nikolaos Mavridis Alfonso Valencia Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment
description	Abstract Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded S I R model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies.
format	article
author	Alberto Ferrari Enrico Santus Davide Cirillo Miguel Ponce-de-Leon Nicola Marino Maria Teresa Ferretti Antonella Santuccione Chadha Nikolaos Mavridis Alfonso Valencia
author_facet	Alberto Ferrari Enrico Santus Davide Cirillo Miguel Ponce-de-Leon Nicola Marino Maria Teresa Ferretti Antonella Santuccione Chadha Nikolaos Mavridis Alfonso Valencia
author_sort	Alberto Ferrari
title	Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment
title_short	Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment
title_full	Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment
title_fullStr	Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment
title_full_unstemmed	Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment
title_sort	simulating sars-cov-2 epidemics by region-specific variables and modeling contact tracing app containment
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1dd0165ab9d2495b8cccf21a610df530
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Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment

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