The Effect of Information-Driven Resource Allocation on the Propagation of Epidemic with Incubation Period

Abstract In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (Susceptible-Exposed-Infected-Susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network, and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period have an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

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Epidemic spreading on evolving networks

International Journal of Modern Physics B ◽

10.1142/s0217979219502667 ◽

2019 ◽

Vol 33 (23) ◽

pp. 1950266 ◽

Cited By ~ 2

Author(s):

Jin-Xuan Yang

Keyword(s):

Preferential Attachment ◽

Small World ◽

Network Evolution ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Scale Free ◽

Evolving Networks ◽

Power Law Exponent ◽

Free Network ◽

Over Time

Network structure will evolve over time, which will lead to changes in the spread of the epidemic. In this work, a network evolution model based on the principle of preferential attachment is proposed. The network will evolve into a scale-free network with a power-law exponent between 2 and 3 by our model, where the exponent is determined by the evolution parameters. We analyze the epidemic spreading process as the network evolves from a small-world one to a scale-free one, including the changes in epidemic threshold over time. The condition of epidemic threshold to increase is given with the evolution processes. The simulated results of real-world networks and synthetic networks show that as the network evolves at a low evolution rate, it is more conducive to preventing epidemic spreading.

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Epidemic threshold and phase transition in scale-free networks with asymmetric infection

The European Physical Journal B ◽

10.1140/epjb/e2008-00370-y ◽

2008 ◽

Vol 65 (4) ◽

pp. 591-594 ◽

Cited By ~ 5

Author(s):

G. Yan ◽

Z.-Q. Fu ◽

G. Chen

Keyword(s):

Phase Transition ◽

Epidemic Threshold ◽

Scale Free ◽

Scale Free Networks

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Traffic-driven epidemic spreading on scale-free networks with tunable degree distribution

International Journal of Modern Physics C ◽

10.1142/s0129183116501254 ◽

2016 ◽

Vol 27 (11) ◽

pp. 1650125 ◽

Cited By ~ 5

Author(s):

Han-Xin Yang ◽

Bing-Hong Wang

Keyword(s):

Power Law ◽

Degree Distribution ◽

Network Structures ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Scale Free ◽

Scale Free Networks ◽

Law Degree

We study the traffic-driven epidemic spreading on scale-free networks with tunable degree distribution. The heterogeneity of networks is controlled by the exponent [Formula: see text] of power-law degree distribution. It is found that the epidemic threshold is minimized at about [Formula: see text]. Moreover, we find that nodes with larger algorithmic betweenness are more likely to be infected. We expect our work to provide new insights in to the effect of network structures on traffic-driven epidemic spreading.

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A New Coupled Awareness-Epidemic Spreading Model with Neighbor Behavior on Multiplex Networks

Complexity ◽

10.1155/2021/6680135 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Chao Zuo ◽

Anjing Wang ◽

Fenping Zhu ◽

Zeyang Meng ◽

Xueke Zhao

Keyword(s):

Transmission Rate ◽

Coupled Model ◽

Dynamical Evolution ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Final Size ◽

Scale Free ◽

Multiplex Networks ◽

Free Network ◽

Effective Transmission

In this paper, we propose a nonlinear coupled model to study the two interacting processes of awareness diffusion and epidemic spreading on the same individual who is affected by different neighbor behavior status on multiplex networks. We achieve this topology scenario by two kinds of factors, one is the perception factor that can change interplay between different layers of networks and the other is the neighbors’ behavior status that can change the infection rate in each layer. According to the microscopic Markov chain approach (MMCA), we analyze the dynamical evolution of the system and derive the theoretical epidemic threshold on uncorrelated heterogeneous networks, and then, we validate the analysis by numerical simulation and discuss the final size of awareness diffusion and epidemic spreading on a scale-free network. With the outbreak of COVID-19, the spread of epidemic in China prompted drastic measures for transmission containment. We examine the effects of these interventions based on modeling of the awareness-epidemic and the COVID-19 epidemic case. The results further demonstrate that the epidemic spreading can be affected by the effective transmission rate of the awareness and neighbors’ behavior status.

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An SIS Epidemic Model with Feedback Mechanism in Scale-Free Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.354 ◽

2011 ◽

Vol 204-210 ◽

pp. 354-358 ◽

Cited By ~ 4

Author(s):

Guang Wu Gong ◽

Da Min Zhang

Keyword(s):

Epidemic Model ◽

Feedback Mechanism ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Spreading Process ◽

Scale Free ◽

Sis Epidemic Model ◽

Scale Free Networks ◽

Controlling Measures ◽

Sis Epidemic

A new susceptible-infected-susceptible model with feedback mechanism is proposed. The dynamic behavior of the epidemic model with feedback mechanism in scale-free networks is researched by theoretical analysis and computer simulation. The results show that feedback mechanism can reduce the stable infective ratio of system; however, it can not influence the epidemic threshold of system. The results can help us to understand rightly epidemic spreading process in reality networks and guide people to design effective epidemic preventive and controlling measures when epidemic outbreaks.

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Self-Awareness-Based Resource Allocation Strategy for Containment of Epidemic Spreading

Complexity ◽

10.1155/2020/3256415 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Xiaolong Chen ◽

Quanhui Liu ◽

Ruijie Wang ◽

Qing Li ◽

Wei Wang

Keyword(s):

Resource Allocation ◽

Network Structure ◽

Coupled Model ◽

Tuning Parameter ◽

Self Awareness ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Optimal Point ◽

Allocation Model ◽

The Impact

Resource support between individuals is of particular importance in controlling or mitigating epidemic spreading, especially during pandemics. However, there remains the question of how we can protect ourselves from being infected while helping others by donating resources in fighting against the epidemic. To answer the question, we propose a novel resource allocation model by considering the awareness of self-protection of individuals. In the model, a tuning parameter is introduced to quantify the reaction strength of individuals when they are aware of the disease. And then, a coupled model of resource allocation and disease spreading is proposed to study the impact of self-awareness on resource allocation and its impact on the dynamics of epidemic spreading. Through theoretical analysis and extensive Monte Carlo simulations, we find that in the stationary state, the system converges to two states: the whole healthy or the completely infected, which indicates an abrupt increase in the prevalence when there is a shortage of resources. More importantly, we find that too cautious and too selfless for the people during the outbreak of an epidemic are both not suitable for disease control. Through extensive simulations, we locate the optimal point, at which there is a maximum value of the epidemic threshold, and an outbreak can be delayed to the greatest extent. At last, we study further the effects of the network structure on the coupled dynamics. We find that the degree heterogeneity promotes the outbreak of disease, and the network structure does not alter the optimal phenomenon in behavior response. Based on the results of this study, a constructive suggestion is that in the face of a global pandemic, individuals or countries should strengthen mutual support and cooperation while doing their own prevention to suppress the epidemic optimally.

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THE SIS MODEL WITH TIME DELAY ON COMPLEX NETWORKS

International Journal of Bifurcation and Chaos ◽

10.1142/s021812740902324x ◽

2009 ◽

Vol 19 (02) ◽

pp. 623-628 ◽

Cited By ~ 14

Author(s):

XIN-JIAN XU ◽

GUANRONG CHEN

Keyword(s):

Complex Networks ◽

Delay Time ◽

Transmission Rate ◽

Small World ◽

Small World Networks ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Exponential Form ◽

Sis Model ◽

Scale Free

We present a time-delayed SIS model on complex networks to study epidemic spreading. We found that the existence of delay will affect, and oftentimes enhance, both outbreak and prevalence of infectious diseases in the networks. For small-world networks, we found that the epidemic threshold and the delay time have a power-law relation. For scale-free networks, we found that for a given transmission rate, the epidemic prevalence has an exponential form, which can be analytically obtained, and it decays as the delay time increases. We confirm all results by sufficient numerical simulations.

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Optimal Control Strategy for Traffic Driven Epidemic Spreading Based on Community Structure

Mathematical Problems in Engineering ◽

10.1155/2013/204093 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Fei Shao ◽

Guo-Ping Jiang

Keyword(s):

Community Structure ◽

Control Strategy ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Optimal Control Strategy ◽

Scale Free ◽

Second Moments ◽

Scale Free Networks ◽

Novel Strategy ◽

The Impact

It is shown that community structure has a great impact on traffic transportation and epidemic spreading. The density of infected nodes and the epidemic threshold have been proven to have significant relationship with the node betweenness in traffic driven epidemic spreading method. In this paper, considering the impact of community structure on traffic driven epidemic spreading, an effective and novel strategy to control epidemic spreading in scale-free networks is proposed. Theoretical analysis shows that the new control strategy will obviously increase the ratio between the first and the second moments of the node betweenness distribution in scale-free networks. It is also found that the more accurate the community is identified, the stronger community structure the network has and the more efficient the control strategy is. Simulations on both computer-generated and real-world networks have confirmed the theoretical results.

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