Acta Geographica Sinica ›› 2022, Vol. 77 ›› Issue (2): 443-456.doi: 10.11821/dlxb202202012

• Population Geography • Previous Articles     Next Articles

Spatial and temporal patterns and factors influencing the spread of the COVID-19 pandemic in China

WANG Boyun1(), LIU Tianyu1, LI Luning1, LI Qiang1(), JIA Pengfei2, CHEN Jin1   

  1. 1. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    2. China Academy of Urban Planning and Design, Beijing 100044, China
  • Received:2020-12-30 Revised:2021-09-17 Online:2022-02-25 Published:2022-04-19
  • Contact: LI Qiang E-mail:wangboyun@mail.bnu.edu.cn;liqiang@bnu.edu.cn
  • Supported by:
    National Key R&D Program of China(2017YFC1503004)

Abstract:

It is essential to unravel the spatial and temporal patterns of the spread of the epidemic in China during the backdrop of the global coronavirus disease 2019 (COVID-19) outbreak in 2020, as the underlying drivers are crucial for scientific formulation of epidemy-preventing strategies. A discriminant model for the spatio-temporal pattern of epidemic spread was developed for 317 prefecture-level cities using accumulated data on confirmed cases. The model was introduced for the real-time evolution of the outbreak starting from the rapid spread of COVID-19 on January 24, 2020, until the control on March 18, 2020. The model was used to analyze the basic characteristics of the spatio-temporal patterns of the epidemic spread by combining parameters such as peak position, full width at half maximum, kurtosis, and skewness. A multivariate logistic regression model was developed to unravel the key drivers of the spatio-temporal patterns based on traffic accessibility, urban connectivity, and population flow. The results of the study are as follows. (1) The straight-line distance of 588 km from Wuhan was used as the effective boundary to identify the four spatial patterns of epidemic spread, and 13 types of spatio-temporal patterns were obtained by combining the time-course categories of the same spatial pattern. (2) The spread of the epidemic was relatively severe in the leapfrogging model. Besides the short-distance leapfrogging model, significant differences emerged in the spatial patterns of the time course of epidemic spread. The peaks of the new confirmed cases in various spatio-temporal patterns were mostly observed on February 3, 2020. The average full widths at the half maximum of all ordinary cities were approximately 14 days, thus, resonating with the incubation period of the COVID-19 virus. (3) The degree of the population correlation with Wuhan city has mainly influenced the spreading and the short-distance leapfrogging spatial patterns. The existence of direct flight from Wuhan city exhibited a positive effect on the long-distance leapfrogging spatial pattern. The number of population outflows has significantly affected the leapfrogging spatial pattern. The integrated spatial pattern was influenced by both primary and secondary epidemic outbreak sites. Thus, cities should pay great attention to traffic control during the epidemic as analysis has shown that the spatio-temporal patterns of epidemic spread in the respective cities can curb the spread of the epidemic from key links.

Key words: COVID-19 outbreak, spatio-temporal pattern, time-course consistency, multivariate logistic regression model, China