An analysis of the domestic resumption of social production and life under the COVID-19 epidemic

Abstract

Population migration and urban traffic are two important aspects of the socioeconomic system. We analyze the trends of social production and resumption of life after the coronavirus disease 2019 (COVID-19)-influenced Spring Festival in 2020 with statistics on reported cases of COVID-19 from China's National Health Commission and big data from Baidu Migration (a platform collecting population migration data). We find that (1) the distribution of COVID-19 cases throughout mainland China has a specific spatial pattern. Provinces in eastern China have more reported cases than those in western China, and provinces adjacent to Hubei have more confirmed COVID-19 cases than nonadjacent provinces. Densely populated regions with well-developed economies and transportation are more likely to have cluster infection incidents. (2) The COVID-19 epidemic severely impacts the return of the migrant population in the Spring Festival travel rush, as demonstrated by the significant reduction in the return scale, along with the extended timespan and uncertainty regarding the end of the travel rush. Among 33 provinces, special administrative regions, autonomous regions and municipalities, 23 of them (approximately 70%) have a return rate below 60%. Hubei, Hong Kong, Xinjiang, and Inner Mongolia have the lowest return rates (below 5%), whereas the return rates in Hainan and Shandong, 272.72% and 97.35%, respectively, indicate the best trend of resumption. Due to government regulations, the population return in densely populated and well-developed regions shows a positive trend. (3) The resumption of urban traffic is slow and varies greatly in different regions. The urban traffic conditions in 22 provinces and municipalities have a more than 60% level of resumption. Guizhou and Yunnan have the highest level of resumption of urban traffic, whereas Xinjiang, Hubei, and Heilongjiang have the lowest (29.37%, 35.76%, and 37.90%, respectively). However, provinces and municipalities with well-developed intercity traffic have a lower level of resumption, mainly because of regulatory methods such as lockdowns and traffic restrictions. The increased public awareness of epidemic prevention and the decreased frequency of outdoor activities are also two positive factors slowing the spread of the epidemic. (4) Time will be necessary to fully resume social production and life throughout China. Xining and Jinan have the highest levels of resumption, 82.14% and 71.51%, respectively. Urumqi and

Analysis of COVID-19 Mortality and Survival in Colombia: A prospective Cohort Study

“…The analysis of sex and its dissimilarities helps to understand the dynamics of the infection and, above all, to direct the strategies for control, treatment, design, and efficacy of the vaccine, and in general health interventions 26 . The analysis by region showed that the most impacted city was Bogotá, followed by the departments of Atlántico and Valle, even though the analysis included the months when the city instituted control strategies including personal care and strict confinement, which, draws attention and alarm to consider at the time of the intervention and the resumption of social production, this following what has been found in capital cities in the world, where the first findings have provided a scientific basis for local governments to make additional decisions on the resumption of work, it is recommended to focus attention mainly on densely populated cities, with the implementation of methods and strategies for the prevention of outbreaks 27 .…”

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

“…We differentiate these studies by their inputs: Cellphone location (Arimura, Ha, Okumura, & Asada, 2020 ; Gao, Rao, et al., 2020 ; Kang, Gao, et al., 2020 ; Kapoor et al., 2020 ; Pepe et al., 2020 ; Roy & Kar, 2020 ; Yabe et al., 2020 ; Ye et al., 2020 ). Internet and social networks: Google (Husnayain, Shim, Fuad, & Su, 2020 ; Kapoor et al., 2020 ; Nguyen et al., 2020 ; Pászto & Vondráková, 2020 ), Baidu (Cheng et al., 2020 ; Liu, Fang, et al., 2020 ; Mu, Yeh, & Zhang, 2020 ; Shen, 2020 ; Shi et al., 2020 ; Tong, Ma, & Liu, 2020 ; Xu, Wang, Dong, Shen, & Xu, 2020 ), Facebook (Coven & Gupta, 2020 ; Heo, Lim, & Bell, 2020 ; Kuchler, Russel, & Stroebel, 2020 ), Twitter geotagged tweets (Iranmanesh & Alpar Atun, 2021 ; Li, Li, et al., 2020 ; Saeed et al., 2021 ; Yang et al., 2020 ), Weibo (Peng, Wang, Liu, & Wu, 2020 ), Quingbo (Zhu, Zheng, Liu, Li, & Wang, 2020 ). Air and sea transport (Depellegrin, Bastianini, Fadini, & Menegon, 2020 ; Snyder & Parks, 2020 ; Sun, Wandelt, & Zhang, 2020 ; Valjarević et al., 2020 ).…”

<p>COVID-19 in Shanghai: IPC Policy Exploration in Support of Work Resumption Through System Dynamics Modeling</p>

“…It is thus extremely important for governments as well as academics to seek approaches and act wisely to make an appropriate balance between economic reopening and epidemic prevention, so as to minimize the adverse social and economic consequence of living with the virus. 37,38 There has been very limited research in this field. 39,40 At the same time, the dynamic systems model presented in this study can serve as an effective means of helping to test work resumption policies for countries around the world.…”