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Rapid urbanization has gradually strengthened the spatial links between cities, which greatly aggravates the possibility of the spread of an epidemic. Traditional methods lack the early and accurate detection of epidemics. This study took the Hubei province as the study area and used Tencent's location big data to study the spread of COVID-19. Using ArcGIS as a platform, the urban relation intensity, urban centrality, overlay analysis, and correlation analysis were used to measure and analyze the population mobility data of 17 cities in Hubei province. The results showed that there was high similarity in the spatial distribution of urban relation intensity, urban centrality, and the number of infected people, all indicating the spatial distribution characteristics of "one large and two small" distributions with Wuhan as the core and Huanggang and Xiaogan as the two wings. The urban centrality of Wuhan was four times higher than that of Huanggang and Xiaogan, and the urban relation intensity of Wuhan with Huanggang and Xiaogan was also the second highest in the Hubei province. Meanwhile, in the analysis of the number of infected persons, it was found that the number of infected persons in Wuhan was approximately two times that of these two cities. Through correlation analysis of the urban relation intensity, urban centrality, and the number of infected people, it was found that there was an extremely significant positive correlation among the urban relation intensity, urban centrality, and the number of infected people, with an R2 of 0.976 and 0.938, respectively. Based on Tencent's location big data, this study conducted the epidemic spread research for "epidemic spatial risk classification and prevention and control level selection" to make up for the shortcomings in epidemic risk analysis and judgment. This could provide a reference for city managers to effectively coordinate existing resources, formulate policy, and control the epidemic.
Subject(s)
COVID-19 , Epidemics , Animals , Humans , Big Data , COVID-19/epidemiology , Disease Outbreaks , CitiesABSTRACT
The COVID-19 pandemic has dramatically altered the way how we communicate with others. From ZOOM to Meta-verse, an increasing number of people are shifting to the virtual world for work and personal life. However, as a technology, virtual reality is still considered merely a device for immersive gaming for the young generation. Thus, despite itspotential, virtual reality is hardly discussed as acore technology enabling Metaverse, which provides a virtual world for everyone. Therefore, it is necessary to examine prior studies for an understanding full spectrum of virtual reality research. There are three primary aims of this study: 1. To trace the history of virtual reality research for providing a holisticview oftheresearch trajectory. 2. Todiscover prevalent topics during the last 34 years as well as highly cited papers and authors. 3. To find hub topics for identifying the direction of interdisciplinary research. © 2022, Tamkang University. All rights reserved.
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The emergence of the new coronavirus in late 2019 further highlighted the human need for solutions to explore various aspects of deadly pandemics. Providing these solutions will enable humans to be more prepared for dealing with possible future pandemics. In addition, it helps governments implement strategies to tackle and control infectious diseases similar to COVID-19 faster than ever before. In this article, we used the social network analysis (SNA) method to identify high-risk areas of the new coronavirus in Iran. First, we developed the mobility network through the transfer of passengers (edges) between the provinces (nodes) of Iran and then evaluated the in-degree and page rank centralities of the network. Next, we developed 2 Poisson regression (PR) models to predict high-risk areas of the disease in different populations (moderator) using the mobility network centralities (independent variables) and the number of patients (dependent variable). The P-value of .001 for both prediction models confirmed a meaningful interaction between our variables. Besides, the PR models revealed that in higher populations, with the increase of network centralities, the number of patients increases at a higher rate than in lower populations, and vice versa. In conclusion, our method helps governments impose more restrictions on high-risk areas to handle the COVID-19 outbreak and provides a viable solution for accelerating operations against future pandemics similar to the coronavirus.
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Recent transformations following the global financial crisis of 2009, COVID-19 pandemic, supply chains disruptions and newest shocks have radically reshaped global production landscape and challenged comparative benefits of global production networks (GPN) vs global value chains (GVC) paradigms in international production analysis. The study tests the hypothesis that GPN concept allows for a better identification of structural shifts in international production structures while revealing regional patterns of cooperation. In the first section, the main methodological constraints of GVC paradigm are specified. Additionally, the reasons for the application of network-based approach to international production are outlined. The second section dissects the EU automotive manufacturing to support the theoretical propositions. While comparing GVC and GPN quantitative toolkits, the possible trade-off has been reached which is to calculate network indicators (transitivity, centrality, etc.) on the inter-country input-output tables. As a result, the hypothesis was confirmed. Specifically, betweenness centrality metric suggests that Czechia and Slovakia have immediately favoured a positive effect of the entry into the EU, whereas neither of GVC indicators reveals such a shift. Simultaneously, 2008 crisis is depicted via GVC indicators, whilst network metrics suggest no structural changes in the production system. These results corroborate to our theoretical juxtaposition of GVC/GPN approaches. The methodological cohesion of two sets of indicators further advances the views on European regional core-periphery integration and automotive production networks dynamics. At the same time, the findings may contribute to the reassessment of regional integration developments in Europe, as well as in Latin America and Eurasia. © González G. H., Sapir E. V., Vasilchenko A. D. Text. 2023.
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The Worldwide spread of the Omicron lineage variants has now been confirmed. It is crucial to understand the process of cellular life and to discover new drugs need to identify the important proteins in a protein interaction network (PPIN). PPINs are often represented by graphs in bioinformatics, which describe cell processes. There are some proteins that have significant influences on these tissues, and which play a crucial role in regulating them. The discovery of new drugs is aided by the study of significant proteins. These significant proteins can be found by reducing the graph and using graph analysis. Studies examining protein interactions in the Omicron lineage (B.1.1.529) and its variants (BA.5, BA.4, BA.3, BA.2, BA.1.1, BA.1) are not yet available. Studying Omicron has been intended to find a significant protein. 68 nodes represent 68 proteins and 52 edges represent the relationship among the protein in the network. A few centrality measures are computed namely page rank centrality (PRC), degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC) together with node degree and Local clustering coefficient (LCC). We also discover 18 network clusters using Markov clustering. 8 significant proteins (candidate gene of Omicron lineage variants) were detected among the 68 proteins, including AHSG, KCNK1, KCNQ1, MAPT, NR1H4, PSMC2, PTPN11 and, UBE21 which scored the highest among the Omicron proteins. It is found that in the variant of Omicron protein-protein interaction networks, the MAPT protein's impact is the most significant.
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COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The restrictions imposed to control the COVID-19 pandemic had significant negative effects on the mental health of the general population, and particularly in nurses as frontline healthcare workers. The main goal of the present study was to analyze the direct and indirect effects, via social connectedness, of centrality of the COVID-19 outbreak on depressive symptoms. Furthermore, it is explored whether this association varied by group (nurses versus general population). The global sample included 326 individuals from the community and 316 nurses, who were administered self-reported questionnaires. Results revealed that event centrality of COVID-19 outbreak was linked to depressive symptoms, both directly and through the deterioration of social connectedness;moreover, this indirect effect was significant for both subsamples. Interventions aimed at preventing the deterioration of social connectedness may facilitate the decrease of depressive symptoms in the aftermath of the pandemic, particularly for nurses. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Purpose: This study aims to empirically examine the relationships among perceived environmental uncertainty (EV), the level of knowledge distance (KD) and the impact of value network on firm performance. Design/methodology/approach: The quantitative analysis is based on data from 243 Chinese companies with engineering, procurement and construction (EPC) business in the context of the COVID-19 pandemic. Findings: The two dimensions of value network [network centrality (NC) and network openness (NO)] have a different impact on firm performance [financial performance (FP) and market performance (MP)]. NC has a positive impact on FP, but not on MP. NO has a positive effect on MP, but not on FP. A reduced KD mediates the relationship between value network and firm performance. Moreover, it fully mediates the relationship between NC and MP, NO and FP. Finally, during the COVID-19 pandemic, only EV has a moderating effect on KD and MP. Research limitations/implications: This study is limited in terms of data set because it relies on a limited amount of cross-sectional data from one specific country. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications: The present findings suggest that EPC professionals should pay more attention to the EV, which may be impacted by policy, technology and the economy. This research has actionable implications for the reform of EPC in the construction industry, and practical recommendations for EPC firms to improve their corporate performance. Originality/value: The results measure the complementary effects of both dimensions of value network (NC and NO) on two distinct aspects of firm performance (MP and FP) and assess the moderating effect of EV and KD in the context of the COVID-19 pandemics. © 2023, Emerald Publishing Limited.
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Scientific analysis of public transport systems at the urban, regional, and national levels is vital in this contemporary, highly connected world. Quantifying the accessibility of nodes (locations) in a transport network is considered a holistic measure of transportation and land use and an important research area. In recent years, complex networks have been employed for modeling and analyzing the topology of transport systems and services networks. However, the design of network hierarchy-based accessibility measures has not been fully explored in transport research. Thus, we propose a set of three novel accessibility metrics based on the k-core decomposition of the transport network. Core-based accessibility metrics leverage the network topology by eliciting the hierarchy while accommodating variations like travel cost, travel time, distance, and frequency of service as edge weights. The proposed metrics quantify the accessibility of nodes at different geographical scales, ranging from local to global. We use these metrics to compute the accessibility of geographical locations connected by air transport services in India. Finally, we show that the measures are responsive to changes in the topology of the transport network by analyzing the changes in accessibility for the domestic air services network for both pre-covid and post-covid times. © 2023 Faculty of Transport and Aviation Engineering, Silesian University of Technology. All rights reserved.
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The ranking of nodes in a network according to their centrality or "importance” is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it informs the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by these issues, in this paper we review some popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network that takes into account the community-based structure of society. In particular, we use the information of the relevance of individuals in the network to take a control decision, i.e., which individuals should be tested, and possibly quarantined. Finally, we also review the extension of these ranking methods to weighted graphs, and explore the importance of weights in a contact network by exhibiting a toy model and comparing node rankings for this case in the context of disease spread. [ FROM AUTHOR] Copyright of International Journal of Control is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Recent studies in network science and control have shown a meaningful relationship between the epidemic processes (e.g., COVID-19 spread) and some network properties. This paper studies how such network properties, namely clustering coefficient and centrality measures (or node influence metrics), affect the spread of viruses and the growth of epidemics over scale-free networks. The results can be used to target individuals (the nodes in the network) to flatten the infection curve. This so-called flattening of the infection curve is to reduce the health service costs and burden to the authorities/governments. Our Monte-Carlo simulation results show that clustered networks are, in general, easier to flatten the infection curve, i.e., with the same connectivity and the same number of isolated individuals they result in more flattened curves. Moreover, distance-based centrality measures, which target the nodes based on their average network distance to other nodes (and not the node degrees), are better choices for targeting individuals for isolation/vaccination.
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Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.
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This study was designed to test a regression model of event centrality and contingencies of self-worth as predictors of posttraumatic growth (PTG), drawing from Constructivist Self Development Theory (CSDT;Saakvitne et al., 1998). In order to participate in the study, participants had to experience at least one adverse childhood experience (ACE) and had to be 18 years or older. Individuals who experienced sexual abuse before 18 years old were excluded from the study to prevent significant discomfort to these individuals (Mersky et al., 2019). Due to conducting the study during a global pandemic, COVID-19 questions were included in the study and used in the correlations, regression model, and post-hoc analyses. Significant results included event centrality and God's love were positively correlated with PTG. Other contingencies of self-worth were not significantly correlated with PTG. Two demographic variables (marital status and education level) were significantly correlated with PTG and used as covariates in the regression model. One result indicated COVID-19 impacting participant's answers was significantly correlated with PTG, and thus, this also was used as a covariate in the regression model. Event centrality and God's love were found to significantly predict PTG in a sequential multiple regression. Post-hoc analyses suggested ACEs affected participants' coping skills during the COVID-19 pandemic, changes in self-worth occurred as a result of the pandemic, and participants made meaning of their traumatic experiences. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Purpose: The current study aims to test perceived affiliate and courtesy stigma in Hubei province China during the early periods of COVID-19 by using network analysis. Method: In this study, 4,591 participants (3,034 female, mean age = 26.64) from the Hubei Province of China were recruited to conduct network analysis. Results: The network analysis found network connections between Estranged - Blamed, Shamed - No Strong Point, and Rejected - Plague were the strongest. The most important stigma features (nodes) of COVID-19 (i.e. Plague, No Strong Point, Discriminated, and Disgusting). Discussion and Conclusions: This study uncovered the most central features of perceived affiliate and courtesy stigma on COVID-19, proposing these features (and associations between features) could be prioritized for anti-stigma interventions for the COVID-19 pandemic. © 2023 Taylor & Francis.
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Global trade including energy trade is expected to suffer a significant contraction as a result of the COVID-19 pandemic. In this paper, we try to measure the impact of the COVID-19 pandemic on the national status and international energy trade patterns. We apply the networks theory to quantify the dynamic process of the international energy trade network in the year 2018–2020, deriving the centrality to capture both national economic status and its topologic characteristics. Under the COVID-19, multilateral energy trade was blocked, thereby some resource-exporting countries show a downward rank of the centrality, and the opposite situation is in higher levels of economic development. By using the community detection method, we also found that new small communities detached from communities that formed before the COVID-19, but geographical related patterns of international energy trade network communities were not affected by the COVID-19 pandemic.
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We examined whether perceived similarity in COVID-19 centrality (i.e., the extent to which one thinks of the pandemic as shaping current and future life) is associated with family relationship quality during the pandemic. Thinking that other family members are similar to oneself regarding the pandemic's centrality may improve the quality of family relationships. We collected data from Turkish family triads (i.e., mother, father, 18–25 years old child) and had 481 participants from 180 families. Participants rated their similarity in COVID-19 centrality with the other two family members and reported the general and daily quality of their relationship with them (relationship satisfaction, closeness, conflict). We analyzed the data using the Social Relations Model. We found that family members who, on average, perceived more similarity in COVID-19 centrality reported higher levels in positive attributes of general relationship quality (i.e., satisfaction and closeness). The effects on conflict and daily relationship quality were less conclusive. This research confirms that family members' reactions during the COVID-19 pandemic are interdependent. Perceiving that other family members are of similar minds about the centrality of the pandemic relates positively to some aspects of relationship quality. © 2023 International Association for Relationship Research.
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Background There are over 30,000 scientific journals with close to two million articles being published each year. We explore how text mining and graph analytics can be used to streamline the process of identifying relevant papers within a specific subject area making the process more objective and reducing bias. Methods A broad search criterion deployed in PubMed, IEEE and ACM search engines returns a set of titles and s. Text mining routines are used to split the into sentences and then Named Entity Recognition plus Linking to the Universal Medical Language System (UMLS) ontology (NER +L) applied to each sentence to identify clinical concepts. A graph was created for all concepts occurring in the same sentence and then the concepts were ranked using eigenvector centrality scores. The overall period covering all s was then split into several sub-periods and for each sub-period graphs created, and the concepts ranked. Results A search for epilepsy treatment in children returned 34k s over a period from 1950 to-date. The s were sub-divided into 12 sub-periods including 2020-21 and 2022. Having created graphs for all s and each subperiod, a common set of concepts across all periods was identified these were then ed from the sub-period ranked lists. The ranked concepts for 2020-21 identified 'COVID-19' and 'lockdown' as being newly used concepts. The rankings for 2022 identified new genes and medications which are being researched, as well as indicating which medications are falling in research interest. Conclusions This study demonstrates the feasibility of using text mining and graph analytics to objectively identify papers for manually review given a broad search criterion. This approach is both efficient and reduces biasand is applicable to any domain/clinical area without requiring an extensive domain knowledge.
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The adverse effects of the high-power energy consumption by cryptocurrencies on the environment and sustainability have raised the interest of a large body of policymakers and market participants. We apply a network approach to investigate the dependency across clean energy, green markets, and cryptocurrencies from 1 January 2018 to 30 November 2021. Our results indicate that sustainable investments, particularly DJSI and ESGL, play a pivotal role in the network system during the COVID-19 crisis. We find that green bonds are the least integrated with the other financial markets, suggesting their significant role in providing diversification benefits to investors. Rolling windows estimation shows that the dependency across the examined marked increased sharply during the COVID-19 crisis, especially between March 2020 and March 2021, after which it faded and became weak and stable until the end of the sample period. Results of the centrality network are consistent with the dependency network analysis. © 2023
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COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Turnover among nurses has been recognized as a frequent and enduring problem in healthcare worldwide. The widespread nursing shortage has reached the level of a healthcare crisis. The COVID-19 pandemic has illustrated the importance of understanding the contributing factors of nurse turnover, and more importantly how to mitigate the problem. Using cross-sectional survey data collected from 3370 newly licensed nurses working across 51 metropolitan areas within 35 U.S. states, we explore how role overload and work constraints can both diminish job satisfaction and increase turnover intentions of new nurses. Coworker support and work role centrality are identified as moderators of these relationships which show potential to mitigate these negative outcomes. This study highlights the importance of coworker support and work centrality in improving job satisfaction and subsequent turnover intentions among newly licensed nurses.