Geographically varying relationships between population flows from Wuhan and COVID-19 cases in Chinese cities

Xu, Gang; Wang, Wenwu; Lu, Dandan; Lu, Binbin; Qin, Kun; Jiao, Limin

doi:10.1080/10095020.2021.1977093

Cited by 18 publications

(9 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the network structure analysis and visualization, we find that the largest clusters of infection in most provinces included cases with a history of residence or travelling in Wuhan, then gathering activities lead to a larger transmission range. Besides, clusters of infection in some areas mostly formed after February 2020, which may be caused by gathering activities during the Spring Festival [ 59 , 60 , 61 , 62 ]. This is another proof that control of gathering activities and quarantine of infected populations are especially critical in the prevention and control of the epidemic.…”

Section: Discussionmentioning

confidence: 99%

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Yang

Zhang

Gao

et al. 2022

IJERPH

View full text Add to dashboard Cite

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

show abstract

Section: Discussionmentioning

confidence: 99%

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Yang

Zhang

Gao

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“… where is the sensitivity weight of , and is the number of input variables. If , then is fit to the network; thus eliminating from the input variable set will end up in a significant decrease in the error rate of the prediction model (Xu et al 2021 ). Although identifying the impact of agents is not properly possible with this method, what affects a complex and unexplained system behaviour can be tested.…”

Section: Methodsmentioning

confidence: 99%

“…set will end up in a significant decrease in the error rate of the prediction model (Xu et al 2021). Although identifying the impact of agents is not properly possible with this method, what affects a complex and unexplained system behaviour can be tested.…”

Section: Stepwise Sensitivity Analysismentioning

confidence: 99%

“…All these complexities have been the key motivator for scholars to investigate the dynamics of urban mobility patterns (Blommaert et al 2017 ). As obtaining mobility data—especially from smartphones—became easier in the last decade, research at the urban and regional scale discussing issues related to transportation (Calabrese et al 2013 ; Huang et al 2018 ; Gao et al 2019 ), economics (Dong et al 2017 ), health (Arimura et al 2020 ; Xu et al 2021 ), and environment (Ford et al 2016 ; Lu et al 2016 ) have commonly seen.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding mobility dynamics using urban functions during the COVID-19 pandemic: comparison of pre-and post-new normal eras

Hayrullahoğlu

Varol

2022

Asia-Pac J Reg Sci

View full text Add to dashboard Cite

Several changes in urban mobility patterns have been observed in response to COVID-19 since March 2020 when the World Health Organization (WHO) declared COVID-19 a pandemic. However, whether mobility changes are entirely pandemic related has not been investigated for each urban function. This paper evaluates the potential determinants of urban mobility changes for six different urban functions during the COVID-19 pandemic via community mobility data collected by Google. Using artificial neural networks (ANN), the dynamics that affect mobility changes in the following urban functions; grocery/pharmacy, residential, parks, retail/recreation, transport stations, and workplaces, were analysed for Çankaya, one of the densest districts in the capital of Turkey. Results of the prediction model show that responses to the pandemic differed considerably by urban function. Before the new normal era, changes in urban mobility trends were strongly dependent on the pandemic as a public health threat and represented by restrictive government measures. However, impacts of the pandemic on intracity mobility decreased in the new normal era when rules were relaxed. These results are useful for developing proactive policies to ensure rapid post-pandemic recovery in urban economics and planning.

show abstract

“…Liu et al 2019), public health (e.g. Wang et al 2019;Xu et al 2021), agriculture (e.g. Harris et al 2017), and environmental science (e.g.…”

Section: Introductionmentioning

confidence: 99%

High-performance solutions of geographically weighted regression in R

Murakami

et al. 2022

Geo-spatial Information Science

Self Cite

View full text Add to dashboard Cite

As an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today's digital world. In this study, we propose two highperformance R solutions for GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniques, respectively GWR-MP and GWR-CUDA. We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models (GWmodel), Multi-scale GWR (MGWR) and Fast GWR (FastGWR). Results showed that all five solutions perform differently across varying sample sizes, with no single solution a clear winner in terms of computational efficiency. Specifically, solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size. For a large sample size, GWR-MP and FastGWR provided coherent solutions on a Personal Computer (PC) with a common multi-core configuration, GWR-MP provided more efficient computing capacity for each core or thread than FastGWR. For cases when the sample size was very large, and for these cases only, GWR-CUDA provided the most efficient solution, but should note its I/O cost with small samples. In summary, GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones, where for certain data-rich GWR studies, they should be preferred.

show abstract

Geographically varying relationships between population flows from Wuhan and COVID-19 cases in Chinese cities

Cited by 18 publications

References 44 publications

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Understanding mobility dynamics using urban functions during the COVID-19 pandemic: comparison of pre-and post-new normal eras

High-performance solutions of geographically weighted regression in R

Contact Info

Product

Resources

About