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This paper proposes a new compound model to investigate the dynamic linkage mechanism between the natural gas and crude oil markets from the multi-scale perspective. In the proposed model, two main steps are involved: multi-scale analysis and network research. Based on the bivariate empirical mode decomposition (BEMD) and Fine-to-Coarse algorithm, the multi-scale analysis ensures the relationship between price fluctuations can be studied under three different time-scales, which corresponding to market disequilibrium, significant events, and long term trend. By integrating the grey correlation degree and Coarse-Gaining algorithm, the network research reveals the dynamic spillover effects of the two markets at different time-scales. To capture the different linked characteristic in different periods, the sample data of Henry Hub and WTI spot prices from 1997 to 2017 is divided into three periods. We transfer the daily correlations between the price fluctuations with specific time-scale into the correlation patterns and establish the network based on their transmission relations. The crucial correlation patterns and the significant transmission relations are revealed by some network indicators. And the proposed approach in this paper can be applied to other field of research. • A new compound model is proposed based on multi-scale analysis and complex network. • The spillover effects of the two markets under three inner factors are studied. • The linkage mechanisms of the two markets at different time-scales are varying. • The important correlation patterns and transmission relations are revealed. [ABSTRACT FROM AUTHOR]

Human awareness plays an important role in the spread of infectious diseases and the control of propagation patterns. Exploring the interplay between human awareness and epidemic spreading is a topic that has been receiving increasing attention. Considering the fact, some well-known diseases only spread between different species we propose a theoretical analysis of the Susceptible–Infected–Susceptible (SIS) epidemic spread from the perspective of bipartite network and risk aversion. Using mean field theory, the epidemic threshold is calculated theoretically. Simulation results are consistent with the proposed analytic model. The results show that, the final infection density is negative linear with the value of individuals’ risk awareness. Therefore, the epidemic spread could be effectively suppressed by improving individuals’ risk awareness. [ABSTRACT FROM AUTHOR]

This paper presents the parametric modified limited penetrable visibility graph (PMLPVG) algorithm for constructing complex networks from time series. We modify the penetrable visibility criterion of limited penetrable visibility graph (LPVG) in order to improve the rationality of the original penetrable visibility and preserve the dynamic characteristics of the time series. The addition of view angle provides a new approach to characterize the dynamic structure of the time series that is invisible in the previous algorithm. The reliability of the PMLPVG algorithm is verified by applying it to three types of artificial data as well as the actual data of natural gas prices in different regions. The empirical results indicate that PMLPVG algorithm can distinguish the different time series from each other. Meanwhile, the analysis results of natural gas prices data using PMLPVG are consistent with the detrended fluctuation analysis (DFA). The results imply that the PMLPVG algorithm may be a reasonable and significant tool for identifying various time series in different fields. [ABSTRACT FROM AUTHOR]

This paper studies the influence of Brent oil price fluctuations on the stock prices of China’s two distinct blocks, namely, the petrochemical block and the electric equipment and new energy block, applying the Shannon entropy of information theory. The co-movement trend of crude oil price and stock prices is divided into different fluctuation patterns with the coarse-graining method. Then, the bivariate time series network model is established for the two blocks stock in five different periods. By joint analysis of the network-oriented metrics, the key modes and underlying evolutionary mechanisms were identified. The results show that the both networks have different fluctuation characteristics in different periods. Their co-movement patterns are clustered in some key modes and conversion intermediaries. The study not only reveals the lag effect of crude oil price fluctuations on the stock in Chinese industry blocks but also verifies the necessity of research on special periods, and suggests that the government should use different energy policies to stabilize market volatility in different periods. A new way is provided to study the unidirectional influence between multiple variables or complex time series. [ABSTRACT FROM AUTHOR]

Fluctuations in prices of natural gas significantly affect global economy. Therefore, the research on the characteristics of natural gas price fluctuations, turning points and its influencing cycle on the subsequent price series is of great significance. Global natural gas trade concentrates on three regional markets: the North American market, the European market and the Asia-Pacific market, with North America having the most developed natural gas financial market. In addition, perfect legal supervision and coordinated regulations make the North American market more open and more competitive. This paper focuses on the North American natural gas market specifically. The Henry Hub natural gas spot price time series is converted to a visibility graph network which provides a new direction for macro analysis of time series, and several indicators are investigated: degree and degree distribution, the average shortest path length and community structure. The internal mechanisms underlying price fluctuations are explored through the indicators. The results show that the natural gas prices visibility graph network (NGP-VGN) is of small-world and scale-free properties simultaneously. After random rearrangement of original price time series, the degree distribution of network becomes exponential distribution, different from the original ones. This means that, the original price time series is of long-range negative correlation fractal characteristic. In addition, nodes with large degree correspond to significant geopolitical or economic events. Communities correspond to time cycles in visibility graph network. The cycles of time series and the impact scope of hubs can be found by community structure partition. [ABSTRACT FROM AUTHOR]

In this paper, we explore an evolutionary vaccination game in the modified activity driven network by considering the closeness. We set a closeness parameter p which is used to describe the way of connection between two individuals. The simulation results show that the closeness p may have an active role in weakening both the spreading of epidemic and the vaccination. Besides, when vaccination is not allowed, the final recovered density increases with the value of the ratio of the infection rate to the recovery rate λ / μ . However, when vaccination is allowed the final density of recovered individual first increases and then decreases with the value of λ / μ . Two variables are designed to identify the relation between the individuals’ activities and their states. The results draw that both recovered and vaccinated frequency increase with the increase of the individuals’ activities. Meanwhile, the immune fee has less impact on the individuals’ vaccination than the closeness. While the λ / μ is in a certain range, with the increase of the value of λ / μ , the recovered frequency of the whole crowds reduces. Our results, therefore, reveal the fact that the best of intentions may lead to backfire. [ABSTRACT FROM AUTHOR]

Considering the difference of different individuals’ physical quality and antibody, this paper investigates the epidemic spreading model with the virus mutation. By using the mean-field theory, the epidemic threshold with degree-dependent spreading rate can be theoretical drawn. According to the numerical simulations, we can obtain that the average infected virus version in the BA network is less than the ER network. In addition, if the effective spreading rate is either small or large enough, the average virus version of the whole infected individuals will reduce. However, when the spreading rate takes some proper values, the average infected virus version can greatly increase. Finally, we study how the different initial infected nodes influence the average virus version of the whole infected individuals. The numerical results show that the greater of the initial infected degree, the smaller of the average virus version of the whole infected individuals. [ABSTRACT FROM AUTHOR]

In this paper, we propose two novel models of epidemic spreading by considering the activity-driven and the network modular. Firstly, we consider the susceptible–infected–susceptible ( S I S ) contagion model and derive analytically the epidemic threshold. The results indicate that the epidemic threshold only involves with the value of the spread rate and the recovery rate. In addition, the asymptotic refractory density of infected nodes in the different communities exhibits different trends with the change of the modularity-factor. Then, the infected-driven vaccination model is presented. Simulation results illustrate that the final density of vaccination will increase with the increase of the response strength of vaccination. Moreover, the final infected density in the original-infected-community shows different trends with the change of the response strength of vaccination and the spreading rate. The infected-driven vaccination is a good way to control the epidemic spreading. [ABSTRACT FROM AUTHOR]

Considering that memory and conformity could affect the agent’s decision, in this paper, we propose a novel model to describe the vaccination dilemma by entangling the spreading dynamics with an evolutionary framework. Our results indicate that if the individuals make decision mostly depending on their own payoffs and do not believe too much in the celebrity, the final infected number will be significantly reduced. Comparing with the individuals in the BA network, people in the ER network escape from contagion much more easily and could get bigger payoffs. For the countries, strengthening the medical security system and reducing the cost of immunity can curb the spread of viruses effectively. From an individual’s viewpoint, people just remember their own last season’s payoffs can urge them to vaccinate. [ABSTRACT FROM AUTHOR]

This paper uses the high-frequency oil price after ensemble empirical mode decomposition, combines crude oil stocks and the US dollar index to construct co-movement threshold networks according to different periods divided by multiple structural breakpoint method. The results show that in the period of sharp fluctuations in oil prices, the inverse relationship between the US dollar index and high-frequency oil prices is more obvious, and the co-movement network of the three is closer to the "small world network". After oil prices returned to stable fluctuations, coupled with the weakening role of the United States in leading the global economy, the reverse relationship between the US dollar index and high-frequency oil prices has gradually weakened, and the reverse relationship between stocks and high-frequency oil prices has become increasingly prominent. The research not only reveals the main co-movement characteristics of the three, but also provides a new idea for future research on oil price fluctuations and its influencing factors. [ABSTRACT FROM AUTHOR]

Abstract: A universal bipartite model is proposed based on an energy supply–demand network. The analytical expression of SPL distribution of the node weight, the “shifting coefficient” and the scaling exponent are presented without edge weight growth by using the mean-field theory approach. The numerical results of SPL distribution of the node weight, the “shifting coefficient” and the scaling exponent with edge weight growth are also presented. The production’s SPL distribution of the US coal enterprizes from 1991 to 2009 is obtained from the empirical analysis. The numerical results obtained from the model are in good agreement with the empirical results. [Copyright &y& Elsevier]

This paper proposes a new compound model to investigate the linkage effect between economic policy uncertainty (EPU) and West Texas Intermediate (WTI) crude oil future price from 1985 to 2018. Two main steps are involved in this proposed model: multi-scale analysis and time delay cross correlation analysis. Considering nonlinear and nonstationary characteristics of crude oil price, the long-term, medium-term, and short-term trends of WTI price are correspondingly obtained by decomposition and reconstruction. To capture the linked characteristics in different periods, we segment the sample data into 14 periods. Finally, we analyze the linkage effect between EPU index and crude oil price based on direction of link, time lag and association strength. The results indicate that information will be transmitted from EPU index to crude oil price regardless of the time scale, but the transmission capacity is different. The period of reverse linkage between oil price and EPU index accounts for 63% of the sample interval, whereas the same linkage mainly occurs when oil price rises. The linkage lag and association strength of EPU index and oil price have obvious dissimilarity in different periods. Potential application value for energy policy makers and oil related investors are presented. • A model is proposed based on multi-scale and time delay cross correlation analysis. • Lag linkage effect between EPU index and crude oil price are studied. • Linkage direction, lag and association strength between them are varying with time. • The linkage difference between them when EPU index is high or low is revealed. [ABSTRACT FROM AUTHOR]

Faced with the high-dimensional characteristics of time series, symbolization is usually used to reduce the dimensionality and effectively eliminate redundancy and irrelevant features. Several existing network prediction methods, which based on the topology of complex networks, only consider the target sequence itself, which ignored the influence of related factors. In this paper, we improve the single-layer network and construct a Multi-layer Symbolic Pattern Network (MSPN) prediction model by considering the interaction between different variables with time lag. The basic idea is to link the directed weighted networks based on causation entropy (CSE), then extract the multi-layer network topology to jointly predict the target sequence. The 2018/1–2018/12 fluctuation trend of crude oil futures prices are predicted, with auxiliary variables including crude oil supply, demand and the three major U.S. stock indexes (S&P 500 Index, NASDAQ Composite Index, and Dow Jones Industrial Average). The results show that the multi-layer network improves the directional prediction accuracy of oil price over the single-layer network, where demand and Dow Jones Industrial Average's auxiliary perform best. It is worth noting that considering auxiliary variables can be effective in improving the accuracy of the model. The more auxiliary layers are not the better, generally considering two auxiliary layers, that is, the three-layer network prediction model can achieve higher accuracy. • A novel prediction model is proposed based on complex network and causation entropy. • The principle of maximum entropy is used to calculate the threshold of symbolization. • The multi-layer network prediction model has higher directional prediction accuracy. [ABSTRACT FROM AUTHOR]

The distance between different regions has a lot of impact on the individuals’ mobility behavior. Meanwhile, the individuals’ mobility could greatly affect the epidemic propagation way. By researching the individuals’ mobility behavior, we establish the coupled dynamic model for individual mobility and transmission of infectious disease. The basic reproduction number is theoretically obtained according to the next-generation matrix method. Through this study, we may get that the stability state of the epidemic system will be prolonged under a higher commuting level. The infection density is almost the same in different regions over a sufficiently long time. The results show that, due to the individual movement, the origin of virus can only speed up or delay the outbreak of infectious diseases, however, it have little impact on the final infection size. [ABSTRACT FROM AUTHOR]

Since 2012, the Middle East has seen a steady rise in the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). A fractional derivative of the non-singular Mittag-Leffler type is used in this research to conduct a mathematical analysis of the dynamics of MERS-CoV infection transmission. The dynamics of such a disease with an additional degree of freedom and non-singular behavior are discovered through the use of the aforementioned fractional operator, and this is one of the important components of our prepared paper. Using the concept of fixed point theory, the existence and uniqueness of solutions are demonstrated. The stability analysis is also tested with the help of the Ulam–Hyers approach, respectively. The numerical solution has been conducted by using the fractional Adams–Bashforth scheme. In the numerical simulation, all classes are demonstrated through the graphical presentation regarding the changing values of fractional-order at time t. The results at various fractional-order laying between (0,1] are drawn with the help of Matlab. We also provide a comparison of the proposed approach with that of the Caputo operator. The outcomes that were achieved illustrate that the considered scheme is highly methodical and very efficient compared to the Caputo fractional operator. • The dynamics of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) into fractional derivative is studied in the sense of non-singular kernels. • The considered problem is analyzed for existence and uniqueness of solution with fractional derivative. • The approximate solution of the proposed problem is obtained by the numerical iterative technique of Adams–Bashforth. • Comparison between the proposed scheme and the Caputo operator is also provided. • The outcomes illustrate that the considered scheme is highly methodical and very efficient compared to the Caputo fractional operator. [ABSTRACT FROM AUTHOR]