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, 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.