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

Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks