Analysis of the Geographic Transmission Differences of COVID-19 in China Caused by Population Movement and Population Density

Abstract

The coronavirus disease (COVID-19) has led to a global pandemic and caused huge healthy and economic losses. Non-pharmaceutical interventions, especially contact tracing and social distance restrictions, play a vital role in the control of COVID-19. Understanding the spatial impact is essential for designing such a control policy. Based on epidemic data of the confirmed cases after the Wuhan lockdown, we calculate the invasive reproduction numbers of COVID-19 in the different regions of China. Statistical analysis indicates a significant positive correlation between the reproduction numbers and the population input sizes from Wuhan, which indicates that the large-scale population movement contributed a lot to the geographic spread of COVID-19 in China. Moreover, there is a significant positive correlation between reproduction numbers and local population densities, which shows that the higher population density intensifies the spread of disease. Considering that in the early stage, there were sequential imported cases that affected the estimation of reproduction numbers, we classify the imported cases and local cases through the information of epidemiological data and calculate the net invasive reproduction number to quantify the local spread of the epidemic. The results are applied to the design of border control policy on the basis of vaccination coverage.

Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data

“…As a center of the regional economy, Wuhan's high population density and rapid population movement resulted in the "siphon effect" that served as a catalyst for the spread of infectious diseases (70, 71), On the other hand, for the cities in the northwest, such as Shennongjia and Enshi, due to the long straightline distance and high commuting cost, the urban centrality and urban connection intensity levels were reflected in the scale of epidemic infection in the region. This also proved that the scale of epidemic infection was highly correlated with urban relation intensity and urban centrality (72)(73)(74).…”

“…In addition, it is not easy to isolate infected people for testing because of the incubation period, which was one of the main reasons for the implementation of the "lockdown" policy in many cities (75). Given that the traditional methods of epidemic detection and control have been unable to check the spread of COVID-19 (76), it is imperative to identify the scale and transmission pace of the epidemic based on new data and methods, quickly and promptly isolate the hardest-hit areas and secure the areas that are at high risk of infection spread (72,77). At the same time, it is also essential to monitor areas closely connected with the hardest-hit cities to prevent the spread of the epidemic on a larger scale (78).…”