Population Movement, City Closure in Wuhan, and Geographical Expansion of the COVID-19 Infection in China in January 2020

Abstract

Background The unprecedented outbreak of 2019-nCoV pneumonia infection in Wuhan City caused global concern, the outflowing population from Wuhan was believed to be a main reason for the rapid and large-scale spread of the disease, so the government implemented a city closure measure to prevent its transmission considering the large amount of travelling before the Chinese New Year.Methods Based on the daily reported new cases and the population movement data between January 1 and 31, we examined the effects of population outflow from Wuhan on the geographical expansion of the infection in other provinces and cities of China, as well as the impacts of the city closure in Wuhan in different scenarios of closing dates. ResultsWe observed a significantly positive association between population movement and the number of the 2019-nCoV cases. The spatial distribution of cases per unit outflow population indicated that some areas with large outflow population might have been underestimated for the infection, such as Henan and Hunan provinces. Further analysis revealed that if the city closure policy was implemented two days earlier, 1420 (95% CI: 1059, 1833) cases could have been prevented, and if Downloaded from httpsA c c e p t e d M a n u s c r i p t 4 two days later, 1462 (95% CI: 1090, 1886) more cases would be possible. ConclusionsOur findings suggest that population movement might be one important trigger for the transmission of 2019-nCoV infection in China, and the policy of city closure is effective to control the epidemic.

Transmissibility of COVID-19 in 11 major cities in China and its association with temperature and humidity in Beijing, Shanghai, Guangzhou, and Chengdu

“…The joint distribution between weather and potential confounders should be taken into account. For example, population movement might trigger the transmission of COVID-19 [45]. As for the effects of interventions, we have plotted the time series of temperatures from January 24, 2020 to February 13, 2020 in Beijing, Shanghai, Guangzhou, and Chengdu in Additional file 1: Figure S1.…”

Environmental contamination of SARS-CoV-2 in healthcare premises

“…3 The COVID-19 outbreak caused a sudden, significant increase in hospital visits from infected and suspected individuals over the course of two months. [4][5][6] The large patient surge overwhelmed hospitals, despite continuous efforts to expand hospital capacity. Hospital waiting times were extended, which increased the time before infected individuals were identified and placed into isolation.…”

Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic

“…Descriptive modeling research that leverages this capability has examined the spatial associations of COVID-19 with socioeconomic and environmental characteristics. This research found, for example, that lower income and income inequality (22), higher temperature and humidity (23), exposure to fine particulate air pollution (24), and mobility and transportation (25,26) were associated with a higher prevalence of COVID-19 cases or mortality. GIS&T also offers approaches to investigating statistical spatial effects and spatial heterogeneity, such as spatial autoregressive models and geographically weighted regression, to account for modeling geographic processes such as spatial diffusion and the variation in relationships among variables over space (27,28).…”