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Understanding the Collective Responses of Populations to the COVID-19 Pandemic in Mainland China
Haoyi Xiong; Ji Liu; Jizhou Huang; Siyu Huang; Haozhe An; Qi Kang; Ying Li; Dejing Dou; Haifeng Wang.
Affiliation
  • Haoyi Xiong; Baidu Inc.
  • Ji Liu; Baidu Inc.
  • Jizhou Huang; Baidu Inc.
  • Siyu Huang; Baidu Inc.
  • Haozhe An; Baidu Inc.
  • Qi Kang; Baidu Inc.
  • Ying Li; Baidu Inc.
  • Dejing Dou; Baidu Inc.
  • Haifeng Wang; Baidu Inc.
Preprint in English | medRxiv | ID: ppmedrxiv-20068676
ABSTRACT
Timely information acquisition and stay-at-home measures have been considered as two effective steps that every person could take to help contain the coronavirus (COVID-19) pandemic. From the perspectives of information and mobility, this work aims at evaluating to what degree the massive population has responded to the emergencies of the COVID-19 pandemic in China. Using the real-time and historical data collected from the Baidu Maps and Baidu search engines, we confirm the strong correlation between the local pandemic situation in every major Chinese city and the population inflows from Wuhan between 1 January and 23 January 2020. We further evidence that, in cities under more critical situations, people are likely to engage COVID-19-related searches more frequently, while they are not likely to escape from the cities. Finally, the correlation analysis using search and mobility data shows that well-informed individuals are likely to travel less, even while the overall travel demands are low compared to the historical records. Partial correlation analysis has been conducted to test the significance of these observations with respect to other controlling factors.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
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