Spatio-temporal propagation of COVID-19 pandemics

Abstract

The coronavirus known as COVID-19 has spread worldwide since December 2019. Without any vaccination or medicine, the means of controlling it are limited to quarantine and social distancing. Here we study the spatio-temporal propagation of the first wave of the COVID-19 virus in China and compare it to other global locations. We provide a comprehensive picture of the spatial propagation from Hubei to other provinces in China in terms of distance, population size, and human mobility and their scaling relations. Since strict quarantine has been usually applied between cities, more insight into the temporal evolution of the disease can be obtained by analyzing the epidemic within cities, especially the time evolution of the infection, death, and recovery rates which affected by policies. We compare the infection rate in different cities in China and provinces in Italy and find that the disease spread is characterized by a two-stages process. In early times, of the order of few days, the infection rate is close to a constant probably due to the lack of means to detect infected individuals before infection symptoms are observed. Then at later times it decays approximately exponentially due to quarantines. This exponential decay allows us to define a characteristic time of controlling the disease which we found to be approximately 20 days for most cities in China in marked contrast to different provinces in Italy which are characterized with much longer controlling time indicating less efficient controlling policies. Moreover, we study the time evolution of the death and recovery rates which we found to show similar behavior as the infection rate and reflect the health system situation which could be overloaded.

What variables can better predict the number of infections and deaths worldwide by SARS-CoV-2? Variation through time

“…in China found a strong correlation between the number of infected individuals in each province studied and the population migration from Hubei to those provinces 51 with a slight decay along time, which agrees with our results although remaining highly significant. Tuli et al (2020) show in their model that countries with higher air passenger traffic have higher likelihood of getting higher level of infections 52 .…”

Spatial analysis and GIS in the study of COVID-19. A review

“…Returning to the spatiotemporal analyses carried out in China, Gross et al (2020) agree that the spread of the disease originated in the Hubei migration prior to confinements. With more information on the dynamics of the virus, in this work they drew imaginary lines connecting vertices representing the epicentres of the virus, specific provinces, cities and locations scattered around the world.…”

“…One of the most important properties of epidemics is their spatial spread, “a characteristic which mainly depends on the epidemic mechanism, human mobility and control strategy” ( Gross et al, 2020 : 2). We can use GIS and spatial statistics to respond to this, and also to help mitigate the epidemic through scientific information, find spatial correlations with other variables, and identify transmission dynamics ( Xiong et al, 2020 ).…”

Built environment and early infection of COVID-19 in urban districts: A case study of Huangzhou

“…As one of the most important characteristics of an epidemic, the spatial spread mainly depends on the transmission mechanism, human mobility, and control strategy ( Gross et al, 2020 ; Rahmani & Mirmahaleh, 2021 ). Responding to the virus spread, GIS and spatial statistics are applicable to track the epidemic through scientific information, such as finding the spatial correlation with related variables and identifying transmission dynamics ( Ellis et al, 2016 ; Xiong, Wang, Chen, & Zhu, 2020 ).…”