Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

Peng, Zhenghong; Wang, Ru; Liu, Lingbo; Wu, Hao

doi:10.3390/ijgi9060402

Cited by 70 publications

(69 citation statements)

References 26 publications

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“…Although COVID-19 was reported several months ago [1], the coronavirus is still raging on a global scale, and is especially surging in the United States, which is one of the most important engines of the global economic network. The pandemic in the United States will have an important impact on the global economy and politics.…”

Section: Introductionmentioning

confidence: 99%

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

Peng

Liu

et al. 2021

IJERPH

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Background: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Methods: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Results: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007–0.157 (mean = 0.048), 7.31–185.6 (mean = 38.89), and 0.04–2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7–14.0) and IFR was 0.70% (95%CI 0.52–0.95%) at T212. Interpretation: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

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Section: Introductionmentioning

confidence: 99%

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

Peng

Liu

et al. 2021

IJERPH

Self Cite

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“…With the rapid development of information and communication technology (ICT), big data has been widely used in the field of public health [11][12][13]. After the outbreak of the epidemic, domestic and foreign scholars conducted in-depth studies on the regional epidemic situation based on big data, combined with mathematical models and spatial analysis, and confirmed cases in various regions.…”

Section: Introductionmentioning

confidence: 99%

Transmission and control pressure analysis of the COVID-19 epidemic situation using multisource spatio-temporal big data

Wang

Tan

et al. 2021

PLoS ONE

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Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the research area, this paper used OD cluster analysis based on Baidu migration data from January 11 to January 25 (before the sealing-off of Wuhan) and concluded that there is a significant correlation 1the migration level from Wuhan to the GBA and the epidemic severity index. This paper also analyzed the migration levels and diffusivity of the outer and inner cities of the GBA. Lastly, four evaluation indexes were selected to research the possibility of work resumption and the rating of epidemic prevention and control through kernel density estimation. According to the study, the amount of migration depends on the geographical proximity, relationship and economic development of the source region, and the severity of the epidemic depends mainly on the migration volume and the severity of the epidemic in the source region. The epidemic risk is related not only to the severity of the epidemic in the source region but also to the degree of urban traffic development and the degree of urban openness. After the resumption of work, the pressure of epidemic prevention and control has been concentrated mainly in Shenzhen and Canton; the further away a region is from the core cities, the lower the pressure in that region. The mass migration of the population makes it difficult to control the epidemic effectively. The study of the relationship between migration volume, epidemic severity and epidemic risk is helpful to further analyze transmission types and predict the trends of the epidemic.

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“…Some scholars have studied the environmental perception and urban calculation of major emergency based on VGI data, surveyed a sample of Twitter messages under the earthquake disaster, and conducted research in the areas of geographic entity identification [ 17 ], detection of regional anomalies [ 18 ], analysis of the happiness level of different cities [ 19 ], and determination of public attitudes [ 13 ]. In addition, Cross regional migration of population [ 20 ] and seeking help on social media [ 21 ] have also been applied and discussed during this pandemic. These studies have broadened social media public opinion research ideas and fully proved that geospatial information plays an indispensable role in social media public opinion analysis.…”

Section: Introductionmentioning

confidence: 99%

A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling

Zhang

Chen

et al. 2020

IJERPH

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The online public opinion is the sum of public views, attitudes and emotions spread on major public health emergencies through the Internet, which maps out the scope of influence and the disaster situation of public health events in real space. Based on the multi-source data of COVID-19 in the context of a global pandemic, this paper analyzes the propagation rules of disasters in the coupling of the spatial dimension of geographic reality and the dimension of network public opinion, and constructs a new gravity model-complex network-based geographic propagation model of the evolution chain of typical public health events. The strength of the model is that it quantifies the extent of the impact of the epidemic area on the surrounding area and the spread of the epidemic, constructing an interaction between the geographical reality dimension and online public opinion dimension. The results show that: The heterogeneity in the direction of social media discussions before and after the “closure” of Wuhan is evident, with the center of gravity clearly shifting across the Yangtze River and the cyclical changing in public sentiment; the network model based on the evolutionary chain has a significant community structure in geographic space, divided into seven regions with a modularity of 0.793; there are multiple key infection trigger nodes in the network, with a spatially polycentric infection distribution.

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Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

Cited by 70 publications

References 26 publications

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

Transmission and control pressure analysis of the COVID-19 epidemic situation using multisource spatio-temporal big data

A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling

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