The Spatio-Temporal Characteristics and Influencing Factors of Covid-19 Spread in Shenzhen, China-An Analysis Based on 417 Cases

Int J Environ Res Public Health. 2020 Oct 13;17(20):7450. doi: 10.3390/ijerph17207450.

Abstract

The global pandemic of COVID-19 has made it the focus of current attention. At present, the law of COVID-19 spread in cities is not clear. Cities have long been difficult areas for epidemic prevention and control because of the high population density, high mobility of people, and high frequency of contacts. This paper analyzed case information for 417 patients with COVID-19 in Shenzhen, China. The nearest neighbor index method, kernel density method, and the standard deviation ellipse method were used to analyze the spatio-temporal characteristics of the COVID-19 spread in Shenzhen. The factors influencing that spread were then explored using the multiple linear regression method. The results show that: (1) The development of COVID-19 epidemic situation in Shenzhen occurred in three stages. The patients showed significant hysteresis from the onset of symptoms to hospitalization and then to diagnosis. Prior to 27 January, there was a relatively long time interval between the onset of symptoms and hospitalization for COVID-19; the interval decreased thereafter. (2) The epidemic site (the place where the patient stays during the onset of the disease) showed an agglomeration in space. The degree of agglomeration constantly increased across the three time nodes of 31 January, 14 February, and 22 February. The epidemic sites formed a "core area" in terms of spatial distribution and spread along the "northwest-southeast" direction of the city. (3) Economic and social factors significantly impacted the spread of COVID-19, while environmental factors have not played a significant role.

Keywords: COVID-19 outbreak; Shenzhen City; epidemic site; influencing factors; spatio-temporal characteristics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19
  • China / epidemiology
  • Cities / epidemiology
  • Coronavirus Infections / epidemiology
  • Coronavirus Infections / therapy
  • Coronavirus Infections / transmission*
  • Humans
  • Pandemics*
  • Pneumonia, Viral / epidemiology
  • Pneumonia, Viral / therapy
  • Pneumonia, Viral / transmission*
  • Risk Factors
  • Socioeconomic Factors
  • Spatio-Temporal Analysis