Competing spreading processes and immunization in multiplex networks

Epidemic spreading on physical contact network will naturally introduce the human awareness information diffusion on virtual contact network, and the awareness diffusion will in turn depress the epidemic spreading, thus forming the competing spreading processes of epidemic and awareness in a multiplex networks. In this paper, we study the competing dynamics of epidemic and awareness, both of which follow the SIR process, in a two-layer networks based on microscopic Markov chain approach and numerical simulations. We find that strong capacities of awareness diffusion and self-protection of individuals could lead to a much higher epidemic threshold and a smaller outbreak size. However, the self-awareness of individuals has no obvious effect on the epidemic threshold and outbreak size. In addition, the immunization of the physical contact network under the interplay between of epidemic and awareness spreading is also investigated. The targeted immunization is found performs much better than random immunization, and the awareness diffusion could reduce the immunization threshold for both type of random and targeted immunization significantly.     

Spreading of epidemics on scale-free networks with nonlinear infectivity

In this paper, we study the spreading of epidemics on scale-free networks with infectivity which is nonlinear in the connectivity of nodes. We will show that the nonlinear infectivity is more appropriate than constant or linear ones, and give the epidemic threshold of the SIS model on a scale-free network with nonlinear infectivity. In addition, we compare the effects of nonlinear infectivity on the epidemic threshold with two other cases on infinite and finite scale-free networks, and find some new results, such as: with unit recovery rate and nonlinear irrational infectivity, the epidemic threshold is always positive; and the epidemic threshold can increase with network size on finite networks, contrary to the findings in all previous work.     

加权无标度网络中的疾病传播

提出具有加权传播率和非线性传染能力的SIR模型和SIS模型,通过平均场方法证明了这两个模型在加权无标度网络中可以存在非零的传播阈值,从而传播率需要跨越更大的传播阈值才能流行.并且得到的结果在特殊情况下可退化为已有的一些经典结论.     

A note on the global behaviour of the network-based SIS epidemic model

In this note we introduce the study of the global behaviour of the network-based SIS epidemic model recently proposed by Pastor-Satorras and Vespignani [Epidemic spreading in scale-free networks, Phys. Rev. Lett. 86 (2001) 3200], characterized in case of homogeneous scale-free networks by a very small epidemic threshold, and extended by Olinky and Stone [Unexpected epidemic threshold in heterogeneous networks: the role of disease transmission, Phys. Rev. E 70 (2004) 03902(r)]. We show that the above model may be read as a particular case of the classical multi-group SIS model proposed by Lajmainovitch and Yorke [A deterministic model for gonorrhea in a nonhomogeneous population, Math. Biosci. 28 (1976) 221] and extended by Aronsson and Mellander [A deterministic model in biomathematics. Asymptotic behaviour and threshold conditions, Math. Biosci. 49 (1980) 207]. Thus, by applying the methods used for SIS multi-group models, we straightforwardly show, for the first time, that the local conditions identified in the physics literature also determine the global behaviour of a disease spreading on a network. Finally, we briefly study the case in which the force of infection is non-linear, by showing that multiple coexisting equilibria are possible, and by giving a global threshold condition for the extinction.     

Topology independent SIS process: An engineering viewpoint

A Susceptible-Infected-Susceptible (SIS) spreading process, occurring on complex networks that are characterized by a special form of contact dynamics called “acquisition exclusivity”, is studied. Assuming statistical independence of joint events, we find analytic solutions for the stationary probabilities that network nodes are infected, and more importantly, find that these solutions are independent of the network topology. We explore the possibilities to use the studied set-up as an engineering solution for controlled spreading on technological networks. In that context, an example for controlled sharing of viral countermeasures in networks is presented. Considering the high epidemic threshold that characterizes the process, “acquisition exclusivity” is suggested as a method for eradication of viral infections from networks.     

Emergence of epidemics in rapidly varying networks

We describe a simple model mimicking disease spreading on a network with dynamically varying connections, and investigate the dynamical consequences of switching links in the network. Our central observation is that the disease cycles get more synchronized, indicating the onset of epidemics, as the underlying network changes more rapidly. This behavior is found for periodically switched links, as well as links that switch randomly in time. We find that the influence of changing links is more pronounced in networks where the nodes have lower degree, and the disease cycle has a longer infective stage. Further, when the switching of links is periodic we observe finer dynamical features, such as beating patterns in the emergent oscillations and resonant enhancement of synchronization, arising from the interplay between the time-scales of the connectivity changes and that of the epidemic outbreaks.     

The dynamics of spreading and immune strategies of sexually transmitted diseases on scale-free network

We examine epidemic threshold and dynamics for sexually transmitted diseases (STDs) spread using a multiple susceptible-infected-removed-susceptible ODE model on scale-free networks. We derive the threshold for the epidemic to be zero in infinite scale-free network. For a hard cut off scale-free network, we also prove the stability of disease-free equilibrium and the persistence of STDs infection. The effects of two immunization schemes, including proportional scheme and targeted vaccination, are studied and compared. We find that targeted strategy compare favorably to a proportional scheme in terms of effectiveness. Theory and simulations both prove that an appropriate condom using has prominent effect to control STDs spread on scale-free networks.     

Multiple effects of self-protection on the spreading of epidemics

Aside from the commonly considered strategies: vaccination or risk, in this work another basic policy self-protection strategy is incorporated into research of epidemics spreading. Then within the network-theoretical framework, we mainly explore the impact of self-protection strategy on the epidemic size and the eradication of infection. Interestingly, we find that the self-protection influence is multiple: given that the effectiveness of the self-protective strategy is negligible, nobody is willing to take up this act, both vaccination and risk traits dominate the whole system; On the contrary, when the effectiveness of self-protective policy is elevated, it becomes a popular strategy and the size of epidemic can be controlled at a relatively low level. However, one worse situation is present as well: when the effectiveness of self-protection is moderate, the infection probability and epidemic size can reach the maximal level. This is because that, under such a case, the emergence of the self-protective strategy neither inspires the enthusiasm of vaccination nor provides ideal effect.     

Range of lower bounds

Each of n jobs is to be processed without interruption on a single machine. Each job becomes available for processing at time zero. The objective is to find a processing order of the jobs which minimizes the sum of weighted completion times added with maximum weighted tardiness. In this paper we give a general case of the theorem that given in [6]. This theorem shows a relation between the number of efficient solutions, lower bound LB and optimal solution. It restricts the range of the lower bound, which is the main factor to find the optimal solution. Also, the theorem opens algebraic operations and concepts to find new lower bounds.     

Analysis of epidemic spreading of an SIRS model in complex heterogeneous networks

In this paper, we study the spreading of infections in complex heterogeneous networks based on an SIRS epidemic model with birth and death rates. We find that the dynamics of the network-based SIRS model is completely determined by a threshold value. If the value is less than or equal to one, then the disease-free equilibrium is globally attractive and the disease dies out. Otherwise, the disease-free equilibrium becomes unstable and in the meantime there exists uniquely an endemic equilibrium which is globally asymptotically stable. A series of numerical experiments are given to illustrate the theoretical results. We also consider the SIRS model in the clustered scale-free networks to examine the effect of network community structure on the epidemic dynamics.     

Weighted inverse maximum perfect matching problems under the Hamming distance

Given an undirected network G(V, E, c) and a perfect matching M ⁰, the inverse maximum perfect matching problem is to modify the cost vector as little as possible such that the given perfect matching M ⁰ can form a maximum perfect matching. The modification can be measured by different norms. In this paper, we consider the weighted inverse maximum perfect matching problems under the Hamming distance, where we use the weighted Hamming distance to measure the modification of the edges. We consider both of the sum-type and the bottleneck-type problems. For the general case of the sum-type case, we show it is NP-hard. For the bottleneck-type, we present a strongly polynomial algorithm which can be done in O(m · n ³).     

Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis

Models for Multiple Criteria Decision Analysis (MCDA) often separate per-criterion attractiveness evaluation from weighted aggregation of these evaluations across the different criteria. In simulation-based MCDA methods, such as Stochastic Multicriteria Acceptability Analysis, uncertainty in the weights is modeled through a uniform distribution on the feasible weight space defined by a set of linear constraints. Efficient sampling methods have been proposed for special cases, such as the unconstrained weight space or complete ordering of the weights. However, no efficient methods are available for other constraints such as imprecise trade-off ratios, and specialized sampling methods do not allow for flexibility in combining the different constraint types. In this paper, we explore how the Hit-And-Run sampler can be applied as a general approach for sampling from the convex weight space that results from an arbitrary combination of linear weight constraints. We present a technique for transforming the weight space to enable application of Hit-And-Run, and evaluate the sampler’s efficiency through computational tests. Our results show that the thinning factor required to obtain uniform samples can be expressed as a function of the number of criteria n as φ(n) = (n − 1)³. We also find that the technique is reasonably fast with problem sizes encountered in practice and that autocorrelation is an appropriate convergence metric.     

Viral conductance: Quantifying the robustness of networks with respect to spread of epidemics

In this paper, we propose a novel measure, viral conductance (VC), to assess the robustness of complex networks with respect to the spread of SIS epidemics. In contrast to classical measures that assess the robustness of networks based on the epidemic threshold above which an epidemic takes place, the new measure incorporates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, we show that VC provides more insight about the robustness of networks than does the epidemic threshold. We also address the paradoxical robustness of Barabási–Albert preferential attachment networks. Even though this class of networks is characterized by a vanishing epidemic threshold, the epidemic requires high effective infection strength to cause a major outbreak. On the contrary, in homogeneous networks the effective infection strength does not need to be very much beyond the epidemic threshold to cause a major outbreak. To overcome computational complexities, we propose a heuristic to compute the VC for large networks with high accuracy. Simulations show that the heuristic gives an accurate approximation of the exact value of the VC. Moreover, we derive upper and lower bounds of the new measure. We also apply the new measure to assess the robustness of different types of network structures, i.e. Watts–Strogatz small world, Barabási–Albert, correlated preferential attachment, Internet AS-level, and social networks. The extensive simulations show that in Watts–Strogatz small world networks, the increase in probability of rewiring decreases the robustness of networks. Additionally, VC confirms that the irregularity in node degrees decreases the robustness of the network. Furthermore, the new measure reveals insights about design and mitigation strategies of infrastructure and social networks.     

Global analysis of multi-strains SIS, SIR and MSIR epidemic models

We consider SIS, SIR and MSIR models with standard mass action and varying population, with n different pathogen strains of an infectious disease. We also consider the same models with vertical transmission. We prove that under generic conditions a competitive exclusion principle holds. To each strain a basic reproduction ratio can be associated. It corresponds to the case where only this strain exists. The basic reproduction ratio of the complete system is the maximum of each individual basic reproduction ratio. Actually we also define an equivalent threshold for each strain. The winner of the competition is the strain with the maximum threshold. It turns out that this strain is the most virulent, i.e., this is the strain for which the endemic equilibrium gives the minimum population for the susceptible host population. This can be interpreted as a pessimization principle.     

Where does the sup norm of a weighted polynomial live?

A characterization is given of the sets supporting the uniform norms of weighted polynomials [w(x)] ⁿ P _n (x), whereP _n is any polynomial of degree at mostn. The (closed) support ∑ ofw(x) may be bounded or unbounded; of special interest is the case whenw(x) has a nonempty zero setZ. The treatment of weighted polynomials consists of associating each admissible weight with a certain functional defined on subsets of ∑ —Z. One main result of this paper states that there is a unique compact set (independent ofn andP _n ) maximizing this functional that contains the points where the norms of weighted polynomials are attained. The distribution of the zeros of Chebyshev polynomials corresponding to the weights [w(x)] ⁿ is also studied. The main theorems give a unified method of investigating many particular examples. Applications to weighted approximation on the real line with respect to a fixed weight are included.     

Spreading dynamics of a SIQRS epidemic model on scale-free networks

In order to investigate the influence of heterogeneity of the underlying networks and quarantine strategy on epidemic spreading, a SIQRS epidemic model on the scale-free networks is presented. Using the mean field theory the spreading dynamics of the virus is analyzed. The spreading critical threshold and equilibria are derived. Theoretical results indicate that the critical threshold value is significantly dependent on the topology of the underlying networks and quarantine rate. The existence of equilibria is determined by threshold value. The stability of disease-free equilibrium and the permanence of the disease are proved. Numerical simulations confirmed the analytical results.     

Epidemic spreading with time delay on complex networks

In this paper, we propose a susceptible-infected-susceptible (SIS) model on complex networks, small-world (WS) networks and scale-free (SF) networks, to study the epidemic spreading behavior with time delay which is added into the infected phase. Considering the uniform delay, the basic reproduction number R ₀ on WS networks and \(\bar R_0\) on SF networks are obtained respectively. On WS networks, if R ₀ ≤ 1, there is a disease-free equilibrium and it is locally asymptotically stable; if R ₀ > 1, there is an epidemic equilibrium and it is locally asymptotically stable. On SF networks, if \(\bar R_0 \leqslant 1\), there is a disease-free equilibrium; if \(\bar R_0 > 1\), there is an epidemic equilibrium. Finally, we carry out simulations to verify the conclusions and analyze the effect of the time delay τ, the effective rate λ, average connectivity 〈k〉 and the minimum connectivity m on the epidemic spreading.     

n-dimensional array languages and description of crystal symmetry—I

Following the general linguistic approach proposed by Narasimhan to pattern recognition we proposed two generative models,viz., the matrix models and the more powerful two-dimensional array models. In this paper we generalize the matrix models ton-dimensions and establish that then-dimensional matrix models are closed under union, concatenation, Kleene closure, ∈-free homomorphism, inverse homomorphism and intersection with regular matrices. Symmetry operations such as translation, reflection and half-turn are examined.     

Nonlinear dynamical analysis and control strategies of a network-based SIS epidemic model with time delay

In this paper, we establish a susceptible-infected-susceptible (SIS) epidemic model with nonlinear incidence rate and time delay on complex networks. Firstly, according to the existence of a positive equilibrium point, we work out the threshold values R₀ and λ_c of disease propagation. Secondly, we demonstrate the stabilities of the disease-free equilibrium point and the disease-spreading equilibrium point by constructing Lyapunov function and applying delay differential equations theorem. Thirdly, four different control strategies are investigated and compared, including uniform immunization control, acquaintance immunization control, active immunization control and optimal control. Finally, we perform representative numerical simulations to illustrate the theoretical results and further discover that the nonlinear incidence rate can more accurately reflect individual psychological activities when a certain disease outbreaks at a high level.     

New conditions for synchronization in complex networks with multiple time-varying delays

In this paper, two kinds of synchronization problems of complex dynamical networks with multiple time-varying delays are investigated, that is, the cases with fixed topology and with switching topology. For the former, different from the commonly used linear matrix inequality (LMI) method, we adopt the approach basing on the scramblingness property of the network’s weighted adjacency matrix. The obtained result implies that the network will achieve exponential synchronization for appropriate communication delays if the network’s weighted adjacency matrix is of scrambling property and the coupling strength is large enough. Note that, our synchronization condition is very new, which would be easy to check in comparison with those previously reported LMIs. Moreover, we extend the result to the case when the interaction topology is switching. The maximal allowable upper bounds of communication delays are obtained in each case. Numerical simulations are given to demonstrate the effectiveness of the theoretical results.