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Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization

The spread of COVID-19 caused by the SARS-CoV-2 virus has become a worldwide problem with devastating consequences. Here, we implement a comprehensive contact tracing and network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disr...

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Autores principales: Serafino, Matteo, Monteiro, Higor S., Luo, Shaojun, Reis, Saulo D. S., Igual, Carles, Lima Neto, Antonio S., Travizano, Matías, Andrade, José S., Makse, Hernán A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053778/
https://www.ncbi.nlm.nih.gov/pubmed/35404949
http://dx.doi.org/10.1371/journal.pcbi.1009865
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author Serafino, Matteo
Monteiro, Higor S.
Luo, Shaojun
Reis, Saulo D. S.
Igual, Carles
Lima Neto, Antonio S.
Travizano, Matías
Andrade, José S.
Makse, Hernán A.
author_facet Serafino, Matteo
Monteiro, Higor S.
Luo, Shaojun
Reis, Saulo D. S.
Igual, Carles
Lima Neto, Antonio S.
Travizano, Matías
Andrade, José S.
Makse, Hernán A.
author_sort Serafino, Matteo
collection PubMed
description The spread of COVID-19 caused by the SARS-CoV-2 virus has become a worldwide problem with devastating consequences. Here, we implement a comprehensive contact tracing and network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints to monitor the evolution of the contact network of disease transmission before and after mass quarantines. As a consequence of the lockdowns, people’s mobility decreases by 53%, which results in a drastic disintegration of the transmission network by 90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, an optimized strategy to break the chain of transmission is to quarantine a minimal number of ‘weak links’ with high betweenness centrality connecting the large k-cores.
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spelling pubmed-90537782022-04-30 Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization Serafino, Matteo Monteiro, Higor S. Luo, Shaojun Reis, Saulo D. S. Igual, Carles Lima Neto, Antonio S. Travizano, Matías Andrade, José S. Makse, Hernán A. PLoS Comput Biol Research Article The spread of COVID-19 caused by the SARS-CoV-2 virus has become a worldwide problem with devastating consequences. Here, we implement a comprehensive contact tracing and network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints to monitor the evolution of the contact network of disease transmission before and after mass quarantines. As a consequence of the lockdowns, people’s mobility decreases by 53%, which results in a drastic disintegration of the transmission network by 90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, an optimized strategy to break the chain of transmission is to quarantine a minimal number of ‘weak links’ with high betweenness centrality connecting the large k-cores. Public Library of Science 2022-04-11 /pmc/articles/PMC9053778/ /pubmed/35404949 http://dx.doi.org/10.1371/journal.pcbi.1009865 Text en © 2022 Serafino et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Serafino, Matteo
Monteiro, Higor S.
Luo, Shaojun
Reis, Saulo D. S.
Igual, Carles
Lima Neto, Antonio S.
Travizano, Matías
Andrade, José S.
Makse, Hernán A.
Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization
title Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization
title_full Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization
title_fullStr Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization
title_full_unstemmed Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization
title_short Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization
title_sort digital contact tracing and network theory to stop the spread of covid-19 using big-data on human mobility geolocalization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053778/
https://www.ncbi.nlm.nih.gov/pubmed/35404949
http://dx.doi.org/10.1371/journal.pcbi.1009865
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