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

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

B. Zhao, X. Wang, et al.

This exciting research led by Baining Zhao and colleagues explores spontaneous mobility changes in Shenzhen following the end of the 'Zero-COVID' policy. With a robust analysis of 148 million travel data points, the study reveals significant spatial discrepancies in mobility patterns, providing crucial insights for future public health strategies against infectious diseases.

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Playback language: English
Abstract
This paper investigates spontaneous mobility changes in Shenzhen, China, following the lifting of the "Zero-COVID" policy in late 2022. Using 148 million travel data points from public transport, the researchers identify spatial discrepancies in mobility patterns, attributing them to heterogeneous responses to the pandemic across different travel purposes and modes. A dynamic model, considering both infection rates and individual travel willingness, is proposed and successfully fits fine-grained urban mobility data. The findings offer valuable insights for informing public health strategies against future large-scale infectious diseases.
Publisher
Humanities and Social Sciences Communications
Published On
May 08, 2024
Authors
Baining Zhao, Xuzhe Wang, Tianyu Zhang, Rongye Shi, Fengli Xu, Fanhang Man, Erbing Chen, Yang Li, Yong Li, Tao Sun, Xinlei Chen
Tags
mobility changes
COVID-19
urban mobility
public health
Shenzhen
travel data
dynamic model

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