The Economic Impact of COVID-19 Crisis Based on Complex Network Evolution Model

The Covid-19 crisis impacts the economy with non-equilibrium and non-linear shocks. This paper builds a trading network model based on the theory of trading economics. Using the network model, the evolutionary procedure of the economic depression triggered by the shocks are researched. The study shows that under the impact of shocks, small and medium-sized trading agents with weak profitability will first experience cash flow crisis. Then the crisis contagion is formed in upstream and downstream through the trading network. The credit reduction caused by the business deterioration will make the interest rate in the economy increase and promote each other with the bankruptcy of trading entities. Eventually, it leads to the feedback loop in liquidity crisis and debt crisis, which accelerates the bankruptcy of enterprises and possibly causing a debt crisis in the banking sector. It is found that after the shock, the economic recovery may take three patterns: stable recovery, slow recession and secondary crisis. Finally, the paper proposes relevant policy recommendations to reduce the impact of the crisis. © 2023 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Topology and evolution of international trade network for fish and fish products

In this study, a complex network method was employed to quantify the changing role of countries in fish trade and the dynamic characteristics of fish globalization. Based on the United Nations Comtrade Database, the International Trade Network for Fish and Fish Products (ITN-Fish) was constructed as a series of weighted-directed networks for each year from 1990 to 2018. Almost all countries and territories worldwide have participated in the fish trade. In 2018, the network identified 229 fish traders. The share of developing countries in imports and exports has increased. Traders actively establish new trade relations, which improve network connectivity. However, these relations only account for a small part of the fish trade. The high connectivity allows risks to spread rapidly in the world through hubs such as the United States and China, which raises concerns about the robustness of these weak links in the Sino-US trade conflict and the outbreak of COVID-19. However, we have optimistic expectations on this issue. The dynamic of network topology property shows that the globalization of fish trade flourished between 1990 and 2018. Although, due to the financial crisis and its subsequent impact, the total amount of fish trade declined in 2009 and 2015, the network structure was not seriously affected, and the trend of topology property remained unchanged. Based on the construction of the international trade network, its node attribute, and its structural attribute, fish trade maintains the trend of globalization. Countries should actively adhere to trade globalization to promote the development of the fish trade.

Research on price transmission in Chinese mining stock market: Based on industry

The strong impact of COVID-19 on the global mining market has caused severe fluctuations in the prices of mineral products and mining stocks. Meanwhile, geopolitical conflicts have exacerbated risks in minerals trade and mining stock transactions. In the face of uncertainties in the international economic landscape and volatility of stock prices, China, as the world's major mineral trading country, has become increasingly linked between its stock market and the mining economy. To clarify the characteristics of mining stock price fluctuations and the evolution of the transmission relationships, and identify the key nodes and main paths of price transmission, we select 100 Chinese mining stocks from January 2019 to October 2022, distinguish them according to the industry category, and use Granger causality test, minimum spanning tree model and complex network analysis method to study. The results show that: (1) Chinese mining stock prices have risen significantly since 2020, and there has been a "decoupling” phenomenon within the stock market, that is, the linkage between some mining stocks has weakened. (2) The stock price fluctuation characteristics and transmission effects of different mining industries are obviously different. Precious metal minerals (PM) have the most dramatic changes in price fluctuations, the most prominent hedging characteristics, and the rapid price response ability, which is the first to accept price transmission. rare earth and rare metal minerals (RE) are sensitive to price fluctuations and are usually the "leader” of the transmission path. Bulk non-ferrous minerals (BNFM) have the most stable price fluctuations and are closely related to other stocks, which is a "transit warehouse” in the transmission path. (3) The price transmission mechanism of Chinese mining stock market has gradually stabilized, and the main transmission paths of "Coal→Agricultural minerals (Agri)→BNFM→Steel” and "PM, Core minerals for new energy (NEM), and RE→BNFM” have been formed in 2022.

Prediction of asymptomatic COVID-19 infections based on complex network.

Novel coronavirus pneumonia (COVID-19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID-19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID-19 transmission model by introducing traditional SEIR (susceptible-exposed-infected-removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound popula-tion flows originating from Wuhan;however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach

Our research contributes a new point of view on China's rare earth dynamic risk spillover measurement;this was performed by combining complex network and multivariate nonlinear Granger causality to construct the time-varying connectedness complex network and analyze the formation mechanism using the impulse response. First, our empirical research found that for the dynamic characteristics of China's rare earth market, due to instability, uncertainty, and geopolitical decisions, disruption can be captured well by the TVP-VAR-SV model. Second, except for praseodymium, oxides are all risk takers and are more affected by the impact of other assets, which means that the composite index and catalysts are main sources of risk spillovers in China's rare earth trading complex network system. Third, from the perspective of macroeconomic variables, there are significant multivariate nonlinear impacts on the total connectedness index of China's rare earth market, and they exhibit asymmetric shock characteristics. These findings indicate that the overall linkage of the risk contagion in China's rare earth trading market is strong. Strengthening the interconnections among the rare earth assets is of important practical significance. Empirical results also provide policy recommendations for establishing trading risk protection measures under macro-prudential supervision. Especially for investors and regulators, rare earth oxides are important assets for risk mitigation. When rare earth systemic trading risk occur, the allocation of oxide rare earth assets can hedge part of the trading risk.

Complex network analysis of volatility spillovers between global financial indicators and G20 stock markets.

This paper analyses the dynamic transmission mechanism of volatility spillovers between key global financial indicators and G20 stock markets. To examine volatility spillover relations, we combine a bivariate GARCH-BEKK model with complex network theory. Specifically, we construct a volatility network of international financial markets utilising the spatial connectedness of spillovers (consisting of nodes and edges). The findings show that spillover relations between global variables and G20 markets vary significantly across five identified sub-periods. Notably, networks are much denser in crisis periods compared to non-crisis periods. In comparing two crisis periods, Global Financial Crisis (2008) and COVID-19 Crisis (2020) periods, the network statistics suggest that volatility spillovers in the latter period are more transitive and intense than the former. This suggests that financial volatility spreads more rapidly and directly through key financial indicators to the G20 stock markets. For example, oil and bonds are the largest volatility senders, while the markets of Saudi Arabia, Russia, South Africa, and Brazil are the main volatility receivers. In the former crisis, the source of financial volatility concentrates primarily in the USA, Australia, Canada, and Saudi Arabia, which are the largest volatility senders and receivers. China emerges as generally the least sensitive market to external volatility.

COVID-19 vaccinations and risk spillovers: Evidence from Asia-Pacific stock markets

This study investigates the impact of COVID-19 vaccinations on volatility (risk) spillovers among major Asia-Pacific stock markets. Utilizing both mean-based and quantile-based connectedness approaches, we examine the evolving patterns and network structure of risk spillovers not only on average but also in the extreme left and right tails. Risk spillovers are typically stronger under extreme shocks. A common regularity observed in the dynamics of standard (average) and extreme risk spillovers is that there are fewer risk spillovers after the launch of the COVID-19 vaccines. We also conduct a series of regression analyses to investigate the association between spillover levels and vaccination rates. The regression results support that an increase in vaccinations is associated with an decrease in standard risk spillovers. Besides, it is observed that vaccinations have an asymmetric impact on the extreme downside-tail and upside-tail risk spillovers. Further, panic sentiment is identified as a potential channel through which vaccinations affect spillovers. Our findings point to the role of COVID-19 vaccinations in stabilizing the Asia-Pacific stock markets by reducing risk spillovers. © 2023 Elsevier B.V.

Research on the Spatiotemporal Evolution of China's Intercity Network Structure from the Perspective of the Relationship between Listed Companies and Customers

The connection between enterprises is an important part of urban connection. Strengthening the analysis of urban functional network based on the connection between enterprises is of great significance to enrich the theoretical research of urban network. Based on the trade relationship data between listed companies and their top five customers from 2010 to 2020, this paper constructs China's urban network, and analyzes the spatio- temporal evolution characteristics of urban network based on the perspective of trade links between enterprises. The research shows that: ① From 2010 to 2020, the urban network scale shows the characteristics of first rising and then falling, and the overall network density is low, ranging from 0.014 to 0.018. The center of gravity of the network presents the trend of "S" - shaped spatial trajectory change and overall southward movement.This feature is consistent with the trend of China's economic center moving southward in recent years. The overall spatial structure of the network changes from coastal to "T" - shaped structure. This feature is consistent with the "T" strategy of China's land development. ② The network traffic is concentrated in a few node cities. The total amount of capital in and out of the top 20 cities accounts for 71.9% of the total capital flow. Beijing and Shanghai are the absolute core of the network. The provincial capitals or sub provincial cities such as Hangzhou, Wuhan, Shenzhen and Guangzhou assume the function of regional centers. Foshan, Qiqihar, Nantong and other manufacturing developed cities are important nodes. It indicates that trade links are more likely to occur in cities with high administrative levels or developed industries. ③ The Pearl River Delta has the highest network density, which is between 0.324 and 0.334. The Yangtze River Delta has the highest total trade flow, which is 78.35 billion yuan. Although the networking level of urban agglomeration in the middle reaches of the Yangtze River and Chengdu Chongqing urban agglomeration is relatively low, they have become an important force to promote the evolution of network structure. ④ The COVID-19 has had a significant impact on the trade flow and network structure of the overall network. The network associations have been further divided and reorganized. The Guangzhou Shenzhen associations have been significantly strengthened. It shows that Guangzhou and Shenzhen have a strong combination effect. The Shanghai associations have been significantly weakened. The research results have a certain reference value for promoting the construction of domestic big cycle and unified big market. © 2023 Journal of Geo-Information Science. All rights reserved.

Debt default, financial risk transmission and governance from the perspective of supply chain network.

The stock risk spillovers of 31 associated enterprises of Evergrande supply chain in China were measured with DCC-GARCH and CoVaR model, and high, moderate and low risk overflow networks in four periods were constructed, finally the overall metrics and dynamic evolution of risk spillover network were explored. The results showed that: With COVID-19 under control in China, the risk spillover of Evergrande supply chain associated enterprises continues to diverge, with the quantity and scope of high risk declining and moderate and low risk rising; The infection scope of high risk spillover has narrowed, from indirect to direct infection; Evergrande subsidiaries play obvious bridge roles in moderate and low risk networks, have strong control over the risk spread; Commercial banks suffer and trigger more risk spillovers, a number of risk spillover groups with commercial banks as cores formed in high and moderate risk networks.

External supply risk of agricultural products trade along the Belt and Road under the background of COVID-19.

Agricultural product trade along the Belt and Road (B&R) is an important part of the international food security system, the vulnerabilities of which have been highlighted by the recent COVID-19 pandemic. Based on the complex network analysis, this study analyzes the characteristics of agricultural products trade network along the B&R. It also combines the effects of COVID-19 with the import trade volume of agricultural products in countries along the B&R to build a risk supply model of agricultural products. The results show that: (1) In 2021, the spatial correlation structure of agricultural products trade along the B&R became increasingly sparse, and the network connectivity and density also decreased. (2) The network showed obvious scale-free distribution characteristics and obvious heterogeneity. Five communities emerged under the influence of the core node countries, but the formation of community in 2021 had obvious geopolitical characteristics. (3) Under the influence of the COVID-19 epidemic, the number of countries with medium-risk and high-risk level along the route facing external dependence risk (REDI), import concentration risk (RHHI) and COVID-19 epidemic risk (RRICI) increased in 2021, and the number of countries with extremely low-risk level decreased. (4) The dominant risk type of external supply of agricultural products along the route changed from compound risk type in 2019 to epidemic risk in 2021. Hence, the results can be expected to prevent external risk impact from reducing excessive concentration of agricultural products trade and excessive dependence on the external market.

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Purpose: COVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network. Design/methodology/approach: Daily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs. Findings: The findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node;however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed. Originality/value: It is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere. © 2022, Emerald Publishing Limited.

Dynamic correlation and risk resonance among industries of Chinese stock market: New evidence from time–frequency domain and complex network perspectives

To sustain market stability, it is crucial to research the impact of risk resonance across industries. In this paper, we demonstrate the dynamic risk resonance between various sectors in the Chinese market. To do so, by using a recently developed method that divides spillover measures based on variance decompositions into their components at different frequency ranges, a set of frequency spillover matrices is obtained to show the overall risk resonance within sectors. Second, we use a complex network to investigate the risk contagion path among different industries. The research results show that: (1) the risk resonance effect varies significantly over time;(2) during our sample period, the transportation and utilities industries are net transmitters;(3) the risk resonance mechanism is frequency dependent. Spillovers generated at low-frequency, extreme occurrences have a long-lasting effect on the industry's risk resonance;and (4) extreme events such as the financial crisis and the COVID-19 will enhance the risk resonance effect. The results of our research can provide a reference for market participants to formulate corresponding regulatory and investment strategies.

Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity;(ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak;and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants. © 2022 Elsevier B.V.

Complex dynamics of COVID-19 mathematical model on Erdős–Rényi network

In this study, a conformable fractional order Lotka–Volterra predator-prey model that describes the COVID-19 dynamics is considered. By using a piecewise constant approximation, a discretization method, which transforms the conformable fractional-order differential equation into a difference equation, is introduced. Algebraic conditions for ensuring the stability of the equilibrium points of the discrete system are determined by using Schur–Cohn criterion. Bifurcation analysis shows that the discrete system exhibits Neimark–Sacker bifurcation around the positive equilibrium point with respect to changing the parameter d and e. Maximum Lyapunov exponents show the complex dynamics of the discrete model. In addition, the COVID-19 mathematical model consisting of healthy and infected populations is also studied on the Erdős Rényi network. If the coupling strength reaches the critical value, then transition from nonchaotic to chaotic state is observed in complex dynamical networks. Finally, it has been observed that the dynamical network tends to exhibit chaotic behavior earlier when the number of nodes and edges increases. All these theoretical results are interpreted biologically and supported by numerical simulations. [ FROM AUTHOR]

Data Mining and Analysis of Prescription Rules for Plague in Ancient Books of Case Records of Qing Dynasty

Objective To analyze the prescription rules for pestilence in ancient books of case records, and provide reference for treatment of coronavirus disease 2019 (COVID19);Methods The medical cases of warm diseases in ancient times were selected as the source before data extraction rules were made. TCM Miner was used to conduct the counts and analysis of association rules, and Cloud Platform of Ancient and Modern Medical Cases was used for complex network analysis. Results A total of141 medical cases were found in the 14 ancient books of case records, involving 66 formulae and 142 Chinese medicinals. The formulae mainly included Xiao Chaihu Tang (Minor Bupleurum Decoction), Dayuan Yin (Membrane Source-Opening Beverage), and JiuweiQianghuo Tang (Nine Ingredients Notopterygium Decoction), while the medicinals mainly included Lianqiao (Fructus Forsythiae), Fuling (Poria), and Shichangpu (RhizomaAcoriTatarinowii). Conlusion The prescriptions against pestilence in ancient times highlight clearing heat and toxins, cooling the blood, resolving dampness and opening the orifices, which is also combined with releasing the exterior. © 2022 IEEE.

Modeling Student Collaboration Network to Enhance Student Interactions

The Covid-19 pandemic, as Henry Kissinger mentions, will not only "forever alter the world order,"but also potentially transform the ever-changing higher education world. The recent increase in technological innovations in information, communications, and computer technology has profoundly transformed traditional teaching-learning processes and peer-to-peer interactions for knowledge transfer. One such radical change in technology that researchers are continually working on is motivating collaborative learning and student interactions to improve their learning experiences. Collaborative Learning (CL), where students work in groups to achieve a specific learning objective, can facilitate a deep learning activity that promotes student participation. However, the potential of discussion forums is limited due to their unstructured nature in LMSs like Canvas.We propose and develop a structured discussion forum that can offer a platform to communicate and discuss problems and receive feedback, discuss solutions, and suggestions online. Students who participate in these discussion forums can benefit in multiple ways, including increased class preparedness and more active learning. The twofold objectives and outcomes include 1) analyzing discussion board data to reveal students' interaction and their degree of participation in the course, and 2) developing a toolset to draw useful inferences from such collaboration networks. Specifically, our schema-based model can help students visualize the discussion board networks creating an engaged learning environment. Furthermore, the model can help draw valuable inferences of the patterns of student interactions and assess student participation and belonging in the course with greater precision.This paper demonstrates a schema-based discussion board model that can allow researchers to collect better-formatted discussion data and more reliable information about the posts, such as the type of posts and the relationships of each post with others. The reimagined discussion boards include the ability to classify discussion posts using various parameters, visualize the posts' patterns of interactions, identify their relationships with other discussion posts, and precisely evaluate student participation in discussions to monitor the major topics of discussion. We believe that the result of increased participation in discussions with other students will have the effect of increasing students' sense of belonging to the community of scholars. © 2022 IEEE.

Immunogenetic perspective of inflammatory disorders

Understanding of the genetic basis underlying inflammatory disorders has progressed in recent years. Contribution of proinflammatory cytokines, human leukocyte antigen (HLA), and non-HLA polymorphisms in the pathogenesis of several autoimmune and immune-mediated inflammatory disorder is critical. HLA plays a central role in disease pathology. Harmful stimuli triggering the signaling mechanisms including nuclear factor-kappa B pathway, Janus kinase-signal transducer and activator of transcription pathway, and mitogen-activated protein kinase pathway results in the release of inflammatory mediators. From acute to chronic inflammation, the etiology of various inflammatory disorders is poorly understood. Inflammatory disorder such as COVID 19 is a devastating havoc to the world. As we reach the end of 2020, >1 million people have succumbed to death worldwide. Disease-manifesting clinical features include mild to severe pneumonia, loss of respiratory function progressing to acute respiratory distress syndrome with occasional multiorgan failure. Cytokine storm, decreased T cell count, and insufficient immune response are conducive issues to COVID 19 pandemic. Varied immune responses to the same antigen across different individuals determine the genetic perspective of disease susceptibility. Through genome-wide association studies, next-generation sequencing and other genetic techniques, several genetic risk loci associated with various inflammatory diseases such as inflammatory bowel disease, psoriasis, sclerosis, and systemic lupus erythematosus (SLE) have been identified. Dysregulated inflammatory pathways, gene mutation, or elevated cytokine level may lead to the disease progression. However, the production of autoantibodies against the nuclear antigens is a hallmark of diseases like SLE and rheumatoid arthritis. Moreover, environmental factors like smoking also increase the risk of inflammatory disorders. Understanding the functional aspects of casual genetic factors underlying the disease pathogenesis greatly facilitates the ability to identify the therapeutic targets relevant to disease. The current chapter deals with the idea of genetic perspective associated with various inflammatory disorders and their potential therapeutic targets along with the factors contributing to disease susceptibility. © 2022 Elsevier Inc. All rights reserved.

Trade for Food Security: The Stability of Global Agricultural Trade Networks.

Global food production is facing increasing uncertainties under climate change and the coronavirus pandemic, provoking challenges and severe concerns to national food security. The role of global agricultural trade in bridging the imbalance between food supply and demand has come to the fore. However, the impact of multifaceted and dynamic factors, such as trade policies, national relations, and epidemics, on the stability of the agricultural trade network (ATN) needs to be better addressed. Quantitatively, this study estimated grouping characteristics and network stability by analyzing the changing global ATN from 1986 to 2018. We found that the evolution of global agricultural trade communities has gone through four stages: the dominance of the US-Asian community, the rise of the European-African community, the formation of tri-pillar communities, and the development of a multipolar community with a more complex structure. Despite witnessing a progressive increase in the nodal stability of the global ATN during the decades, particular gaps can still be found in stability across countries. Specifically, the European community achieved stability of 0.49 and its trade relations were effectively secured. Meanwhile, the remaining leading communities' stability shows a stable and upward trend, albeit with more significant challenges in trade relations among some of them. Therefore, how to guarantee the stability of trade relations and strengthen the global ATN to resist external shocks has become an essential question to safeguard global food security.

Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity;(ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak;and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants.