  As lockdowns and stay-at-home orders start to be lifted across the globe,
governments are struggling to establish effective and practical guidelines to
reopen their economies. In dense urban environments with people returning to
work and public transportation resuming full capacity, enforcing strict social
distancing measures will be extremely challenging, if not practically
impossible. Governments are thus paying close attention to particular locations
that may become the next cluster of disease spreading. Indeed, certain places,
like some people, can be "super-spreaders." Is a bustling train station in a
central business district more or less susceptible and vulnerable as compared
to teeming bus interchanges in the suburbs? Here, we propose a quantitative and
systematic framework to identify spatial super-spreaders and the novel concept
of super-susceptibles, i.e. respectively, places most likely to contribute to
disease spread or to people contracting it. Our proposed data-analytic
framework is based on the daily-aggregated ridership data of public transport
in Singapore. By constructing the directed and weighted human movement networks
and integrating human flow intensity with two neighborhood diversity metrics,
we are able to pinpoint super-spreader and super-susceptible locations. Our
results reveal that most super-spreaders are also super-susceptibles and that
counterintuitively, busy peripheral bus interchanges are riskier places than
crowded central train stations. Our analysis is based on data from Singapore,
but can be readily adapted and extended for any other major urban center. It
therefore serves as a useful framework for devising targeted and cost-effective
preventive measures for urban planning and epidemiological preparedness.
