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• W3132240338 abstract "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." @default.
• W3132240338 created "2021-03-01" @default.
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• W3132240338 date "2021-02-25" @default.
• W3132240338 modified "2023-10-18" @default.
• W3132240338 title "Identification of superspreading environment under COVID-19 through human mobility data" @default.
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• W3132240338 doi "https://doi.org/10.1038/s41598-021-84089-w" @default.
• W3132240338 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7907097" @default.
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