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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9053778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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