Regionalization for infection control: An algorithm for delineating containment zones considering the regularity of human mobility

Restricting human movement to decrease contact probability and frequency helps mitigate large-scale epidemics. Movement-based zoning can be implemented to delineate the boundaries for movement restrictions. Previous studies used network community detection methods, which capture cohesive within-region movements, to delineate containment zones. However, most people usually travel and spend most of their time in several fixed locations, which implies that an infected person could transmit the pathogens to only a specific group of people with whom s/he usually has a contact in frequently-visited locations. Existing network community detection methods cannot reflect the regularity of the flow of people; thus, this study aims to use land-use patterns to reflect trip purposes to measure the regularity of human mobility. We propose a novel network community detection method, the Human Mobility Regularity-based Zoning (HuMoRZ) algorithm, to delineate containment zones incorporating mobility regularity. The Taipei metropolitan area in Taiwan is used to demonstrate the feasibility of the proposed algorithm. The spatial diffusion of an emerging respiratory disease, novel influenza A/H1N1, is simulated for comparing three different quarantine zoning systems: (1) a minimum zoning unit, (2) optimal zoning without considering mobility regularity, and (3) optimal zoning considering mobility regularity. Two epidemiological performance indicators are used to compare simulation results: namely, the accumulated infected number (AN) on the 30th day, reflecting the severity of an epidemic, and the critical time (CT), the moment at which half of the population becomes infected, measuring the diffusion speed of an epidemic. To measure the variety of different facility types within a containment zone, we further use Shannon's entropy scores, representing a self-contained zone, and the boxplot of all zones' entropy scores, reflecting geospatial homogeneity of life functions across zones. Our results suggest that containment zones that incorporate mobility regularity could significantly delay the epidemic peak and critical time and decrease the severity of an epidemic. The zoning patterns proposed in our algorithm could also allow for more life functions in a zone and more evenly distributed life resources across zones than those of zones generated by other methods. These findings could provide insightful implications for fighting the COVID-19 pandemic.