Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

Baining Zhao, Xuzhe Wang, Tianyu Zhang, Rongye Shi, Fengli Xu, Fanhang Man, Erbing Chen, Yang Li, Yong Li, Tao Sun and Xinlei Chen ()
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Baining Zhao: Tsinghua University
Xuzhe Wang: Tsinghua University
Tianyu Zhang: Tsinghua University
Rongye Shi: Beihang University
Fengli Xu: Tsinghua University
Fanhang Man: Tsinghua University
Erbing Chen: Shenzhen University
Yang Li: Tsinghua University
Yong Li: Tsinghua University
Tao Sun: Peng Cheng Laboratory
Xinlei Chen: Tsinghua University

Palgrave Communications, 2024, vol. 11, issue 1, 1-15

Abstract: Abstract Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

Date: 2024
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DOI: 10.1057/s41599-024-03068-4

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