Network percolation reveals adaptive bridges of the mobility network response to COVID-19.

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hengfang Deng, Jing Du, Jianxi Gao, Qi Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/069cb194ebd647e2bbea98d23b751123
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:069cb194ebd647e2bbea98d23b751123
record_format	dspace
spelling	oai:doaj.org-article:069cb194ebd647e2bbea98d23b7511232021-12-02T20:16:24ZNetwork percolation reveals adaptive bridges of the mobility network response to COVID-19.1932-620310.1371/journal.pone.0258868https://doaj.org/article/069cb194ebd647e2bbea98d23b7511232021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258868https://doaj.org/toc/1932-6203Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.Hengfang DengJing DuJianxi GaoQi WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0258868 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hengfang Deng Jing Du Jianxi Gao Qi Wang Network percolation reveals adaptive bridges of the mobility network response to COVID-19.
description	Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.
format	article
author	Hengfang Deng Jing Du Jianxi Gao Qi Wang
author_facet	Hengfang Deng Jing Du Jianxi Gao Qi Wang
author_sort	Hengfang Deng
title	Network percolation reveals adaptive bridges of the mobility network response to COVID-19.
title_short	Network percolation reveals adaptive bridges of the mobility network response to COVID-19.
title_full	Network percolation reveals adaptive bridges of the mobility network response to COVID-19.
title_fullStr	Network percolation reveals adaptive bridges of the mobility network response to COVID-19.
title_full_unstemmed	Network percolation reveals adaptive bridges of the mobility network response to COVID-19.
title_sort	network percolation reveals adaptive bridges of the mobility network response to covid-19.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/069cb194ebd647e2bbea98d23b751123
work_keys_str_mv	AT hengfangdeng networkpercolationrevealsadaptivebridgesofthemobilitynetworkresponsetocovid19 AT jingdu networkpercolationrevealsadaptivebridgesofthemobilitynetworkresponsetocovid19 AT jianxigao networkpercolationrevealsadaptivebridgesofthemobilitynetworkresponsetocovid19 AT qiwang networkpercolationrevealsadaptivebridgesofthemobilitynetworkresponsetocovid19
_version_	1718374447588573184

Network percolation reveals adaptive bridges of the mobility network response to COVID-19.

Ejemplares similares