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This study introduces a stochastic SIR (Susceptible–Infectious–Recovered) model on complex networks, utilizing a scale-free network to represent inter-human contacts. The model incorporates a threshold parameter, denoted as R σ , which plays a decisive role in determining whether the disease will persist or become extinct. When R σ < 1 , the disease exhibits exponential decay and eventually disappear. Conversely, when R σ > 1 , the disease persists. The critical case of R σ = 1 is also examined. Furthermore, we establish a unique stationary distribution for R σ > 1. Our findings highlight the significance of network topology in modeling disease spread, emphasizing the role of social networks in epidemiology. Additionally, we present computational simulations that consider the scale-free network's topology, offering comprehensive insights into the behavior of the stochastic SIR model on complex networks. These results have substantial implications for public health policy, disease control strategies, and epidemic modeling in diverse contexts. [ABSTRACT FROM AUTHOR]

A new study from the University of Idaho examines the impact of different viral strains, lockdown strategies, and vaccination campaigns on epidemic dynamics. The study analyzes three network models and finds that highly connected nodes play a significant role in the spread of infections. Intermittent lockdown strategies with 7-day intervals are effective in reducing the total number of infections. Rapid mass vaccination campaigns are successful in reducing infection rates, but the effectiveness varies depending on the network structure. The study emphasizes the importance of considering network structure for effective pandemic control. [Extracted from the article]

Purpose: The aim of the paper is to analyze the impact of cooperativeness of managers who occupy central positions in interaction networks on the performance and stress levels of a whole organization. Design/methodology/approach: To explore this relationship, a multi-parameter agent-based model is proposed which implements the prisoner's dilemma game approach on a scale-free network in the NetLogo environment. A description of the socioeconomic aspects and the key concepts implemented in the model is provided. Stability and correctness have been tested through a series of validation experiments, including sensitivity analysis. The source code is available for further exploration and testing. Findings: The simulations revealed that improving the stress resistance of all employees moderately increases organizational performance. Analyzing managers' roles showed that increasing only the stress resistance of managers does not account for significantly higher overall performance. However, a substantial increase in organizational performance and a decrease in stress levels are achieved when managers are unconditionally cooperative. This effect is stronger for the lowered stress resistance of employees. Therefore, the willingness of managers to cooperate under all circumstances can be a key factor in achieving better performance and building a more pleasant, stress-free working environment. Originality/value: This paper aims to present a model for analyzing cooperation, specifically in the organizational context, extending the prisoner's dilemma with novel concepts and mechanisms. Although the results confirm the existing theories about the importance of central nodes in complex networks, they also provide further details on how the cooperative behavior of central nodes (i.e. the managers) might benefit the organization. [ABSTRACT FROM AUTHOR]

• The small-world property only for one of the network randomization methods could be established. • The scale-free property was not identified in any earthquake network. • The majority of earthquake networks obtained random structure. • The disputing of the universality of non-trivial properties small-world and scale-free for the Greek seismicity and for the specific network constructions. • The application of the network theory was found to be a powerful tool for the investigation of complex phenomena such as seismic activity. The non-trivial properties of the small-world and scale-free structure are investigated in earthquake networks that are formed from the seismicity taking place in Greece and adjacent area. The nodes of these networks are either well-defined seismic zones or square cells comprised in a normal grid superimposed onto the study area. The network connections are either drawn whenever earthquake succession is observed between the nodes or given by the significant correlation of the seismic activity in two areas. The networks are formed for varying magnitude thresholds and for selected time spans during 1845–2017 to establish that the data are complete for each study period. Rigorous statistical analysis is performed using also appropriate resampling methods to assess the presence of the small-world and scale-free property. The scale-free property was not identified in any network and only for one of the network construction methods the small-world property could be established, disputing the universality of these non-trivial properties because are not verified for the Greek seismicity and for the specific network constructions. [ABSTRACT FROM AUTHOR]

We present a novel SIRS model on scale-free networks that takes into account behavioral memory and time delay to depict an adaptive behavioral feedback mechanism, which can better characterize the actual spread of epidemics. We conduct rigorous analysis on the dynamics of the model, including the basic reproduction number R 0 , uniform persistence and the global asymptotic stability of equilibria. The model has the sharp threshold property, namely, if R 0 is less than 1 then the disease-free equilibrium is globally asymptotically stable while if R 0 is larger than 1 then the endemic equilibrium is globally asymptotically stable. We further perform an optimal control study for the model to seek effective vaccination and treatment strategies. The existence and uniqueness of these optimal control strategies are demonstrated. Finally, we perform some stochastic network simulations that yield quantitative agreement with the deterministic mean-field approach. Our findings indicate that time delay does not affect R 0 , but behavioral memory does. • An SIRS epidemic model considering a adaptive behavior mechanism is studied on scale-free networks. • The basic reproduction number R 0 and equilibria stability of the model are determined. • The optimal time-varying vaccination and treatment strategies with the adaptive feedback are investigated. • The effects of adaptive behavior mechanism on epidemic dynamics and control strategies are studied. [ABSTRACT FROM AUTHOR]

Abstract Many optimization algorithms have adopted scale-free networks to improve the search ability. However, most methods have merely changed their population topologies into those of scale-free networks; their experimental results cannot verify that these algorithms have superior performance. In this paper, we propose a scale-free artificial bee colony algorithm (SFABC) in which the search is guided by a scale-free network. The mechanism enables the SFABC search to follow two rules. First, the bad food sources can learn more information from the good sources of their neighbors. Second, the information exchange among good food sources is relatively rare. To verify the effectiveness of SFABC, the algorithm is compared with the original artificial bee colony algorithm (ABC), several advanced ABC variants, and other metaheuristic algorithms on a wide range of benchmark functions. Experimental results and statistical analyses indicate that SFABC obtains a better balance between exploration and exploitation during the optimization process and that, in most cases, it can provide a competitive performance of the benchmark functions. We also verify that scale-free networks can not only improve the optimization performance of ABC but also enhance the search ability of other metaheuristic algorithms, such as differential evolution (DE) and the flower pollination algorithm (FPA). [ABSTRACT FROM AUTHOR]

Keywords: Wuhan; People's Republic of China; Asia; CDC and FDA; Emerging Technologies; Epidemic; Epidemiology; Machine Learning; Risk and Prevention; Scale-free Network EN Wuhan People's Republic of China Asia CDC and FDA Emerging Technologies Epidemic Epidemiology Machine Learning Risk and Prevention Scale-free Network 837 837 1 08/21/23 20230825 NES 230825 2023 AUG 27 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Data detailed on CDC and FDA have been presented. Keywords for this news article include: Wuhan, People's Republic of China, Asia, CDC and FDA, Emerging Technologies, Epidemic, Epidemiology, Machine Learning, Risk and Prevention, Scale-free Network, Wuhan University. [Extracted from the article]

This paper addresses the problem of designing a robust controller for a class of complex dynamical networks (CDNs). This class of CDNs is characterized by a scale-free typical structure with Rossler and Lorenz-type nodes. Direct control of every node in a CDN with a large number of nodes might be unnecessary. Based on the concept of pinning control of continuous-time chaotic systems and the matrix measure theory, some simple stability criteria are derived to guarantee the asymptotic stability of the system states such that all the system states of the closed-loop system converge towards the desired unstable equilibrium points. Consequently, the proposed controller developed here for scale-free networks can be employed to handle a broader class of CDNs with different types of nodes. Numerical simulations are provided to show the effectiveness of the proposed control method. • Complex dynamical networks CDN. • Pinning prediction-based control. • Chaos. • Nonlinear system. [ABSTRACT FROM AUTHOR]

Keywords: Fukuoka; Japan; Asia; Biological Products; Communicable Disease Control; Emerging Technologies; Environment and Public Health; Health and Medicine; Immunization; Machine Learning; Public Health; Public Health Practice; Scale-free Network; Vaccination; Vaccine Efficacy; Vaccines EN Fukuoka Japan Asia Biological Products Communicable Disease Control Emerging Technologies Environment and Public Health Health and Medicine Immunization Machine Learning Public Health Public Health Practice Scale-free Network Vaccination Vaccine Efficacy Vaccines 183 183 1 06/12/23 20230612 NES 230612 2023 JUN 14 (NewsRx) -- By a News Reporter-Staff News Editor at Vaccine Weekly -- Research findings on Immunization - Vaccine Efficacy are discussed in a new report. The results show that with a higher vaccination cost and/or a lower vaccine efficacy, the stochastic noise has no gap in vaccination coverage (VC) with the default without noise case, but brings a smaller final epidemic size (FES). [Extracted from the article]

In this paper, we investigate robust optimal control of a network-based SIVS epidemic system with an uncertain time delay, which contains time-dependent and degree-dependent vaccination controls. The dynamic behaviours of the delayed system with time-independent controls are discussed. We propose a robust optimal control problem, in which the accumulated density of infected individuals and control costs, and the sensitivity to the uncertain time delay are all involved. Since the objective contains a non-standard sensitivity term, it is difficult to solve this problem using conventional optimization techniques. By introducing an auxiliary initial value system, we transform this problem into a standard form. Furthermore, a numerical scheme which combines the control parametrization scheme with the interior point algorithm is constructed. Finally, numerical simulations with different time delay and weight factor are carried out. We find that, in the case of the basic reproduction number being smaller than one, the optimal control schemes can effectively shorten the disease duration for larger time delays; in face of the disease pandemic, greater control efforts should be made in time at the beginning of disease outbreak, especially for no time delay; in either case, smaller weight factors contribute to minor vibrations of optimal controls and lesser disease survivals. These results can provide a new insight into controlling diseases. • A network-based SIVS system with an uncertain time delay and vaccination controls is developed. • A robust optimal control problem of the delayed SIVS system is investigated. • Effects of the time delay on disease spread are addressed. • Effects of different weight factors on disease control are discussed. [ABSTRACT FROM AUTHOR]

The scale-free network with self-similarity is an essential data structure widely used in social networks and bioinformatics. The subnetwork investigation of large-scale networks has attracted attention in many applications. Traditional methods are computationally complexity and time-consuming. A new methodology needs to handle the computational complexity challenge from large-scale network datasets. In this paper, we develop a series of edge-based subnetwork investigation algorithms: HEPS-1 and HEPS-2. We investigate the relations between edges and their edge neighbours caused by the reconcile of scale-free and self-similarity. Then, we determine the edges for the subnetwork. In this way, we sample the networks while keeping the original statistical characters. The computational complexity is low. The effectiveness of sampling is enhanced. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for analyzing synchronization stability in a complex delayed dynamical network via impulsive control. We present the sufficiency conditions for pinning synchronization stability of single impulsive controller for an undirected complex delayed dynamical network in the presence of identical coupling delays between nodes. We also show that a single impulsive controller can always pin a given complex delayed dynamical network to a homogenous solution, provided that both the coupling strength and coupling delay are properly selected. Subsequently, the results are illustrated by a typical scale-free (SF) network composing of the representative oscillators and a small-world (SW) neuronal network. Numerical simulations with three kinds of the homogenous solutions, including an equilibrium point, a periodic orbit, and a chaotic orbit, are finally given to demonstrate the effectiveness of the proposed control methodology. [ABSTRACT FROM PUBLISHER]

Abstract: An SEIRS epidemic model on the scale-free networks is presented, where the active contact number of each vertex is assumed to be either constant or proportional to its degree for this model. Using the analytical method, we obtain the two threshold values for above two cases and find that the threshold value for constant contact is independent of the topology of the underlying networks. The existence of positive equilibrium is determined by threshold value. For a finite size of scale-free network, we prove the local stability of disease-free equilibrium and the permanence of the disease on the network. Furthermore, we investigate two major immunization strategies, random immunization and targeted immunization, some similar results are obtained. The simulation shows the positive equilibrium is stable. [Copyright &y& Elsevier]

Abstract: Both the threat of bioterrorism and the natural emergence of contagious diseases underscore the importance of quantitatively understanding disease transmission in structured human populations. Over the last few years, researchers have advanced the mathematical theory of scale-free networks and used such theoretical advancements in pilot epidemic models. Scale-free contact networks are particularly interesting in the realm of mathematical epidemiology, primarily because these networks may allow meaningfully structured populations to be incorporated in epidemic models at moderate or intermediate levels of complexity. Moreover, a scale-free contact network with node degree correlation is in accord with the well-known preferred mixing concept. The present author describes a semi-empirical and deterministic epidemic modeling approach that (a) focuses on time-varying rates of disease transmission in both unstructured and structured populations and (b) employs probability density functions to characterize disease progression and outbreak controls. Given an epidemic curve for a historical outbreak, this modeling approach calls for Monte Carlo calculations (that define the average new infection rate) and solutions to integro-differential equations (that describe outbreak dynamics in an aggregate population or across all network connectivity classes). Numerical results are obtained for the 2003 SARS outbreak in Taiwan and the dynamical implications of time-varying transmission rates and scale-free contact networks are discussed in some detail. [Copyright &y& Elsevier]

This paper investigates synchronization dynamics of a general model of complex delayed networks as well as the effects of time delays. Some simple yet generic criteria ensuring delay-independent and delay-dependent synchronization are derived, which are less conservative than those reported so far in the literature. Moreover, a scale ‘v’ denoted by a function of the smallest and the second largest eigenvalues of coupling matrix is presented to analyze the effects of time delays on synchronization of the networks. Furthermore, various kinds of coupling schemes, including small-world networks and scale-free networks, are studied. It is shown that, if the coupling delays are less than a positive threshold, then the network will be synchronized. On the other hand, with the increase of the coupling delays, the synchronizability of the network will be restrained and even eventually desynchronized. The results are illustrated by a prototype composed of the chaotic Duffing oscillators. Numerical simulations are also given to verify theoretical results. [ABSTRACT FROM AUTHOR]

It is now known that the complexity of network topology has a great impact on the stabilization of complex dynamical networks. In this work, we study the control of random networks and scale-free networks. Conditions are investigated for globally or locally stabilizing such networks. Our strategy is to apply local feedback control to a small fraction of network nodes. We propose the concept of virtual control for microscopic dynamics throughout the process with different pinning schemes for both random networks and scale-free networks. We explain the main reason why significantly less local controllers are required by specifically pinning the most highly connected nodes in a scale-free network than those squired by the randomly pinning scheme, and why there is no significant difference between specifically and randomly pinning schemes for controlling random dynamical networks. We also study the synchronization phenomenon of controlled dynamical networks in the stabilization process, both analytically and numerically. [ABSTRACT FROM AUTHOR]

• Novel methods are proposed in this study for building earthquake networks using a declustered catalog • The proposed and designed methods in this study do not require any parameter setting. • The designed networks in this study are scale-free and hierarchical with community structure. • Correlations between earthquake magnitude and node properties were detected in this study. • There are close relationships between detected patterns and seismological laws. In recent years, complex networks have been used as new tools to study patterns in earthquake data. Although various methods have been developed to construct earthquake networks, there is still a long way to use this approach as a complete framework for analyzing seismicity. This research develops novel methods for building earthquake networks and investigates the patterns that they could reveal. The proposed methods use a specific declustered catalog and define nodes based on main shocks and edges on aftershocks' period or sequence. Another method is offered to convert the resulted networks, as earthquake networks, to epicenters networks. The catalog of Iran's earthquakes from 2006 to 2018 is used to produce earthquake networks using the above-mentioned methods. The resulted networks are scale-free and hierarchical with community structure, as expected. Some nodes' features in the networks are shown correlated with the magnitude of the related event. Most patterns and features are preserved while converting the earthquakes network to epicenters network. Proposed methods better capture a region's seismic features into a complex network with a more precise relationship between seismological laws and network characteristics and can help develop perfect seismicity analyzing framework based on complex networks. [ABSTRACT FROM AUTHOR]

• We propose a new framework that considers the imperfect vaccination on the spatially structured and heterogeneous population. • We present a new vaccination update rule: myopic update rule, which is only based on one focal player's own perception regarding the disease outbreak. • Healthy individuals are willing to inoculate the vaccine under the myopic update rule, which can stop the infectious disease from being spread. • In regular lattice, the vaccinated fraction is more sensitive to the vaccine failure rate than the relative vaccine cost. How to effectively prevent the diffusion of infectious disease has become an intriguing topic in the field of public hygienics. To be noted that, for the non-periodic infectious diseases, many people hope to obtain the vaccine of epidemics in time to be inoculated, rather than at the end of the epidemic. However, the vaccine may fail as a result of invalid storage, transportation and usage, and then vaccinated individuals may become re-susceptible and be infected again during the outbreak. To this end, we build a new framework that considers the imperfect vaccination during the one cycle of infectious disease within the spatially structured and heterogeneous population. Meanwhile, we propose a new vaccination update rule: myopic update rule, which is only based on one focal player's own perception regarding the disease outbreak, and one susceptible individual makes a decision to adopt the vaccine just by comparing the perceived payoffs vaccination with the perceived ones of being infected. Extensive Monte-Carlo simulations are performed to demonstrate the imperfect vaccination behavior under the myopic update rule in the spatially structured and heterogeneous population. The results indicate that healthy individuals are often willing to inoculate the vaccine under the myopic update rule, which can stop the infectious disease from being spread, in particular, it is found that the vaccine efficacy influences the fraction of vaccinated individuals much more than the relative cost of vaccination on the regular lattice, Meanwhile, vaccine efficacy is more sensitive on the heterogeneous scale-free network. Current results are helpful to further analyze and model the choice of vaccination strategy during the disease outbreaks. [ABSTRACT FROM AUTHOR]