Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

Abstract Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are sta...

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Autores principales:	Haotian Wang, Abhirup Ghosh, Jiaxin Ding, Rik Sarkar, Jie Gao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fcd28f5bca744bc693da810066f242cc
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spelling	oai:doaj.org-article:fcd28f5bca744bc693da810066f242cc2021-12-02T14:37:39ZHeterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data10.1038/s41598-021-87034-z2045-2322https://doaj.org/article/fcd28f5bca744bc693da810066f242cc2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87034-zhttps://doaj.org/toc/2045-2322Abstract Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.Haotian WangAbhirup GhoshJiaxin DingRik SarkarJie GaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Haotian Wang Abhirup Ghosh Jiaxin Ding Rik Sarkar Jie Gao Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
description	Abstract Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.
format	article
author	Haotian Wang Abhirup Ghosh Jiaxin Ding Rik Sarkar Jie Gao
author_facet	Haotian Wang Abhirup Ghosh Jiaxin Ding Rik Sarkar Jie Gao
author_sort	Haotian Wang
title	Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
title_short	Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
title_full	Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
title_fullStr	Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
title_full_unstemmed	Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
title_sort	heterogeneous interventions reduce the spread of covid-19 in simulations on real mobility data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fcd28f5bca744bc693da810066f242cc
work_keys_str_mv	AT haotianwang heterogeneousinterventionsreducethespreadofcovid19insimulationsonrealmobilitydata AT abhirupghosh heterogeneousinterventionsreducethespreadofcovid19insimulationsonrealmobilitydata AT jiaxinding heterogeneousinterventionsreducethespreadofcovid19insimulationsonrealmobilitydata AT riksarkar heterogeneousinterventionsreducethespreadofcovid19insimulationsonrealmobilitydata AT jiegao heterogeneousinterventionsreducethespreadofcovid19insimulationsonrealmobilitydata
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Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

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