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Spatio-temporal distribution characteristics of COVID-19 in China: a city-level modeling study

Qianqian Ma1,2, Jinghong Gao1,2, Wenjie Zhang1,2

  • 1The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

BMC Infectious Diseases
|August 15, 2021
PubMed

Insights

This study analyzed COVID-19 spread in China, finding early epidemic clusters centered in Hubei province. Spatial clustering decreased over time, highlighting the importance of spatio-temporal analysis for outbreak detection.

Area of Science:

  • Epidemiology
  • Public Health
  • Spatial Statistics

Background:

  • The COVID-19 pandemic necessitated understanding disease spread.
  • Nationwide, city-level spatio-temporal analyses of COVID-19 in China were limited.

Purpose of the Study:

  • To analyze and visualize the spatio-temporal distribution and clustering of COVID-19 cases.
  • To examine data from 362 cities across 31 provinces in mainland China.

Main Methods:

  • Spatio-temporal statistical analysis of confirmed COVID-19 cases (Jan 10–Oct 5, 2020).
  • Employed statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistics.

Main Results:

  • The epidemic's high incidence stage was Jan 17–Feb 9, 2020.
  • Hotspots identified in Hubei province cities (Wuhan, etc.).
  • Early spatial autocorrelation showed moderate clustering (Moran's I max Jan 31), decreasing over time.
  • 19 significant clusters detected; 63.16% from Jan–Feb.
  • Largest cluster centered in Hubei (Wuhan).
  • Cluster scope reduced over time, shifting from broad to city-specific.

Conclusions:

  • Spatio-temporal cluster detection is crucial for understanding epidemic evolution and early warning.
  • Findings offer insights for medical resource allocation and monitoring potential COVID-19 resurgence in China.
Abstract

Keywords:
COVID-19ChinaGeographic hotspotSpatio-temporal distributionTime–space scanVisualization

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