Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis

  • Rui Huang Department of Mathematics and Statistics, College of Science, Wuhan University of Science and Technology, Wuhan, Hubei Province, China
  • Miao Liu Department of Pathology, Brigham and Women's Hospital, Harvard Medical School Boston, MA, United States
  • Yongmei Ding Department of Mathematics and Statistics, College of Science, Wuhan University of Science and Technology, Wuhan, Hubei Province, China
Keywords: COVID-19, Spatial-temporal distribution, Logistic model, SEIR

Abstract

Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control.

Published
2020-03-31
How to Cite
1.
Huang R, Liu M, Ding Y (2020) Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. J Infect Dev Ctries 14:246-253. doi: 10.3855/jidc.12585
Section
Coronavirus Pandemic