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

Area of Science:

• Epidemiology
• Geographic Information Systems (GIS)
• Public Health

Background:

• The COVID-19 pandemic highlighted the critical role of superspreaders in disease transmission.
• Existing research often focuses on individual behavior, neglecting the environmental context of superspreading events.

Purpose of the Study:

• To develop a method for identifying superspreading environments by integrating human mobility data and geographical factors.
• To create a city-level "risk map of superspreading environment" (SE-risk map) for targeted public health interventions.

Main Methods:

• Utilized historical human mobility data to define individuals' generalized activity spaces and space-time prisms.
• Analyzed geographic interconnections of six types of public facilities (bars, shopping centers, karaoke/cinemas, mega malls, libraries, sports centers).
• Integrated spatial risk assessments into a comprehensive SE-risk map.

Main Results:

• The proposed method accurately estimates empirical superspreading environment hot spots.
• The SE-risk map of Hong Kong successfully identified areas overlapping with actual bar-related disease clusters.
• This research is the first to combine facility location and human mobility data for superspreading environment identification.

Conclusions:

• The SE-risk map offers a data-driven approach to identify high-risk environments for disease transmission.
• This method enables more targeted non-pharmaceutical interventions and exit strategies, moving beyond city-wide lockdowns.
• Integrating geographic factors provides a more nuanced understanding of superspreading dynamics.