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

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High-risk areas for coronavirus disease 2019 (COVID-19) in China were linked to economic development and population movement. Controlling these factors can help prevent local transmission.

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Area of Science:

  • Epidemiology
  • Public Health
  • Spatial Analysis

Background:

  • The coronavirus disease 2019 (COVID-19) pandemic poses a significant global public health threat.
  • Understanding the spatial distribution of COVID-19 and its associated socioeconomic factors is crucial for effective epidemic control.

Purpose of the Study:

  • To identify high-risk areas for COVID-19 in China.
  • To investigate the association between socioeconomic factors and the spatial distribution of COVID-19.
  • To provide insights for epidemic control strategies in China and globally.

Main Methods:

  • Analysis of COVID-19 case data from 30 mainland Chinese provinces (excluding Hubei) between January 16 and March 31, 2020.
  • Inclusion of demographic, economic, health, and transportation factors in the analysis.
  • Application of Global Autocorrelation analysis and Bayesian spatial models to identify spatial patterns and risk factors.

Main Results:

  • COVID-19 incidence exhibited significant spatial autocorrelation (Global Moran's I = 0.31, P<0.05).
  • High-risk areas were concentrated around Hubei province and in economically developed regions.
  • Per capita household consumption expenditure (RR=1.887) and the proportion of migrants from Hubei (RR=1.099) were significant socioeconomic risk factors, explaining substantial spatial variation.

Conclusions:

  • COVID-19 risk is positively associated with economic development and population mobility.
  • Strategies to block population movement and reduce local exposure are effective in curbing local COVID-19 transmission.
  • Findings offer valuable guidance for public health interventions during the COVID-19 pandemic.