  The node-place model has been widely used to classify and evaluate transit
stations, which sheds light on individual travel behaviors and supports urban
planning through effectively integrating land use and transportation
development. This article adapts this model to investigate whether and how
node, place, and mobility would be associated with the transmission risks and
presences of the local COVID-19 cases in a city. Similar studies on the model
and its relevance to COVID-19, according to our knowledge, have not been
undertaken before. Moreover, the unique metric drawn from detailed visit
history of the infected, i.e., the COVID-19 footprints, is proposed and
exploited. This study then empirically uses the adapted model to examine the
station-level factors affecting the local COVID-19 footprints. The model
accounts for traditional measures of the node and place as well as actual human
mobility patterns associated with the node and place. It finds that stations
with high node, place, and human mobility indices normally have more COVID-19
footprints in proximity. A multivariate regression is fitted to see whether and
to what degree different indices and indicators can predict the COVID-19
footprints. The results indicate that many of the place, node, and human
mobility indicators significantly impact the concentration of COVID-19
footprints. These are useful for policy-makers to predict and monitor hotspots
for COVID-19 and other pandemics transmission.
