Identification of superspreading environment under COVID-19 through human mobility data

Abstract COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shoppi...

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Autores principales:	Becky P. Y. Loo, Ka Ho Tsoi, Paulina P. Y. Wong, Poh Chin Lai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/829b9e847e2b40c5919ddf5a26d49545
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spelling	oai:doaj.org-article:829b9e847e2b40c5919ddf5a26d495452021-12-02T13:20:12ZIdentification of superspreading environment under COVID-19 through human mobility data10.1038/s41598-021-84089-w2045-2322https://doaj.org/article/829b9e847e2b40c5919ddf5a26d495452021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84089-whttps://doaj.org/toc/2045-2322Abstract COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space–time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a “risk map of superspreading environment” (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.Becky P. Y. LooKa Ho TsoiPaulina P. Y. WongPoh Chin LaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Becky P. Y. Loo Ka Ho Tsoi Paulina P. Y. Wong Poh Chin Lai Identification of superspreading environment under COVID-19 through human mobility data
description	Abstract COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space–time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a “risk map of superspreading environment” (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.
format	article
author	Becky P. Y. Loo Ka Ho Tsoi Paulina P. Y. Wong Poh Chin Lai
author_facet	Becky P. Y. Loo Ka Ho Tsoi Paulina P. Y. Wong Poh Chin Lai
author_sort	Becky P. Y. Loo
title	Identification of superspreading environment under COVID-19 through human mobility data
title_short	Identification of superspreading environment under COVID-19 through human mobility data
title_full	Identification of superspreading environment under COVID-19 through human mobility data
title_fullStr	Identification of superspreading environment under COVID-19 through human mobility data
title_full_unstemmed	Identification of superspreading environment under COVID-19 through human mobility data
title_sort	identification of superspreading environment under covid-19 through human mobility data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/829b9e847e2b40c5919ddf5a26d49545
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Identification of superspreading environment under COVID-19 through human mobility data

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