Human movement is key to the spread of infectious diseases, including COVID-19. Hence, it is also key to the control and containment of the disease itself [1]. The flow of people from places to places is not uniform across all links in a city. Some links—edges of the human flow network connecting two nodes corresponding to two locales—exhibit particularly high flows, while others are experience less intense flows despite the fact that they may connect two busy places. This leads to some heterogeneity in both the spreadability and vulnerability of the disease. Indeed, certain places, like some people, can be “super-spreaders.” Here, we aim at extending the concept of ‘super-spreader’ from complex network analysis to understand the spatial distribution of spatial super-spreaders, and spatial super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it [2]. In this framework, we seek to uncover these specific locales using the daily-aggregated ridership data of public transport in Singapore. Specifically, we developed a systematic way to identify super-spreader and super-susceptible locations based on the integration of human flow intensity with two neighborhood diversity metrics. Our results show that most spatial super-spreaders are also spatial super-susceptibles. Counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. This framework is useful for the post-outbreak reopening and future disease control preparedness.
