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COVID-19 distributes socially in China: A Bayesian spatial analysis

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Listed:
  • Di Peng
  • Jian Qian
  • Luyi Wei
  • Caiying Luo
  • Tao Zhang
  • Lijun Zhou
  • Yuanyuan Liu
  • Yue Ma
  • Fei Yin

Abstract

Purpose: The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other countries control the epidemic. Methods: We analyzed the data of COVID-19 cases from 30 provinces in mainland China (outside of Hubei) from 16 January 2020 to 31 March 2020, considering the data of demographic, economic, health, and transportation factors. Global autocorrelation analysis and Bayesian spatial models were used to present the spatial pattern of COVID-19 and explore the relationship between COVID-19 risk and various factors. Results: Global Moran’s I statistics of COVID-19 incidences was 0.31 (P

Suggested Citation

  • Di Peng & Jian Qian & Luyi Wei & Caiying Luo & Tao Zhang & Lijun Zhou & Yuanyuan Liu & Yue Ma & Fei Yin, 2022. "COVID-19 distributes socially in China: A Bayesian spatial analysis," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-11, April.
    • Handle: RePEc:plo:pone00:0267001
    DOI: 10.1371/journal.pone.0267001
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