How Urban Factors Affect the Spatiotemporal Distribution of Infectious Diseases in Addition to Intercity Population Movement in China

Abstract

The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. During the Chinese New Year holiday, population outflow from Wuhan induced the spread of the epidemic to other cities in China. This study analyzed massive intercity movement data from Baidu and epidemic data to study how intercity population outflows affected the spatiotemporal spread of the epidemic. This study further investigated how urban factors influenced the spatiotemporal spread of COVID-19. The analysis indicates that intercity movement was an important factor in the spread of the epidemic in China, and the impact of intercity movement on the spread was heterogeneous across different classes of cities. The spread of the epidemic also varied among cities and was affected by urban factors including the total population, population density, and gross domestic product (GDP). The findings have implications for public health management. Mega-cities should consider tougher measures to contain the spread of the epidemic compared with other cities. It is of great significance for policymakers in any nation to assess the potential risk of epidemics and make cautious plans ahead of time.

Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases

“…Focusing on health studies on the spatial distribution of COVID-19, some studies show COVID-19 spatial patterns using GIS. There are interesting papers that relate COVID-19 and external environment factors, such as mobility in urban areas [14]. Other papers seek to analyse the spread of the virus indirectly using the characteristics of the main affected areas in terms of incomes, economic activities and population density, among others [15][16][17][18][19].…”

Understanding small Chinese cities as COVID-19 hotspots with an urban epidemic hazard index

“…4 ) show that similar to EpiRank, this ratio does serve as a decent hazard indicator, through which the six epicenters are listed with high ranks. Furthermore, by contrast, shear population and annual GDP, arguably two most considered social-economic indicators of urban regions 6 , are not helpful in revealing the epidemic ground-truth. Conceptually, analysis on EpiRank help us pin down the ratio of local population/inter-city outflow as a critical social-economic factor in explaining the observed phenomenon.…”

“…It implies that the likelihood of epidemic hazards should be high in these regions. Many social-economic factors may account for this fact 6 , 7 : for example, social scientists may observe that these towns are all located in the northeast part of China, where local economies are often underdeveloped, and local residents are often more behavioral active than they are supposed to be in face of the epidemic (e.g., 8 ); other conjectures may attend to the fact that since these are neither coastal cities nor metropolitans where imported cases are more common, local control measures and regulations are thus somewhat relaxed in these regions, which led to heedlessness of early signals (e.g., 9 , 10 ), or that these northern regions have cold winters and also less residential housing space than the south, hence the hazard of severe infections was harbored (e.g., 11 ). Although these arguments are sound, it is desired that quantitative reasoning could be addressed to explain why these small Chinese towns stood out as epidemic hotspots.…”

Could Historical Mortality Data Predict Mortality Due to Unexpected Events?

“…This important human achievement generated a great increase in the world population, as well as major changes in its age distribution. Recently, interest in mortality trends and forecasts has been renewed as policymakers, health scientists, demographers, and economists focus on changing drivers of mortality [2][3][4][5][6][7][8][9][10] and on impacts of pandemics such as COVID-19 [11][12][13].…”