SIS-SEIQR adaptive network model for pandemic influenza
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This paper aims to present an SIS-SEIQR network model for pandemic influenza. We propose a network algorithm to generate an adaptive social network with dynamic hub nodes to capture the disease transmission in a human community. Effects of visiting probability on the spread of the disease are investigated. The results indicate that high visiting probability increases the transmission rate of the disease.Keywords:
Pandemic
Influenza pandemic
Disease Transmission
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Identifying superspreaders in a network is a key problem to designing an effective mitigation strategy against a spread of an epidemic disease. Superspreaders are a set of nodes that play a hub role in a disease spread network, and classical network centrality measures are often used to identify such hubs. In this research, we test a hypothesis that a node's intrinsic property plays a role in the dynamics of disease spreading in a network. Specifically, we test whether spreading of an epidemic disease is affected by a node's property of being an amplifier or attenuator. Using GEM (Global Epidemic Model), we conducted experiments for epidemic spreading on a hypothetical, global network of 155 cities. We find that node's intrinsic property plays a significant role in disease spreading dynamics. Based on these findings we propose a new metric, R0-adjusted centrality.
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We introduce a weighted adaptive network model to investigate the epidemic dynamics based on a susceptible-infective-susceptible (SIS) pattern, where the weight of a link describes the contact strength between two connected individuals. In the model, a susceptible node is able to transfer the weight of the link connecting an infected neighbor to a link connecting one of its susceptible neighbors. It is found that this weight adaption process could strongly aggravate the destructiveness of an epidemic and leads to a new population relation. Moreover, the effectiveness of a simple epidemic control strategy on a weighted adaptive network is examined. The results show that the weight adaption process may reduce the strategy efficiency. Analysis are presented and the results support our numerical simulations.
Weighted network
Network Structure
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Deferent from the other epidemic models, this paper focus on the different spreading features of network viruses compared with biological viruses such as the connectivity rate of the net is variable and that is also the key factor determining the existence of the threshold. Based on a more general epidemic model of the network viruses we constructed in another paper, this paper presents the solution to the model, and then gets the conclusion: if the speed of viruses' propagation varies with the network connectivity rate and their curing rates are relatively small, then the epidemic thresholds do not exist. For the particular case of the connectivity rate the paper carries out the simulation test and finds it is consistent well with the statistics of the real viruses. Thus the paper replies the two open problems about the epidemic model of computer virus model proposed by White.
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Network model
Infection rate
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Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the Susceptible-Infectious-Recovered (SIR) epidemic model on contact networks in the Chinese case. In this paper, we investigate the prediction accuracy of the SIR model on networks also for Italy. Specifically, the Italian regions are a metapopulation represented by network nodes and the network links are the interactions between those regions. Then, we modify the network-based SIR model in order to take into account the different lockdown measures adopted by the Italian Government in the various phases of the spreading of the COVID-19. Our results indicate that the network-based model better predicts the daily cumulative infected individuals when time-varying lockdown protocols are incorporated in the classical SIR model.
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Pandemic
Metapopulation
2019-20 coronavirus outbreak
Network model
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This paper proposed a dynamic social contact network (DSCN) model algorithm which could get the structure characteristics of small-world (SW) network and scale-free (SF) network simultaneity. After discussing the development course of the H1N1 instance and the transmission parameters,studied the trend of pandemic influenza A (H1N1) based on the DSCN model under four countermeasures including isolation,random immunization,acquaintance immunization and target immunization by simulation. The simulation results indicate that target immunization is the most effective to stop the epidemic in the four countermeasures mentioned above. These results support both theory and practice for disease social transmission.
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Isolation
Pandemic
Social distance
Social network (sociolinguistics)
Antibody response
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Epidemics theory has been used in different contexts in order to describe the propagation of diseases, human interactions or natural phenomena. In computer science, virus spreading has been also characterized using epidemic models. Although in the past the use of epidemic models in telecommunication networks has not been extensively considered, nowadays, with the increasing computation capacity and complexity of operating systems of modern network devices (routers, switches, etc.), the study of possible epidemic-like failure scenarios must be taken into account. When epidemics occur, such as in other multiple failure scenarios, identifying the level of vulnerability offered by a network is one of the main challenges. In this paper, we present epidemic survivability, a new network measure that describes the vulnerability of each node of a network under a specific epidemic intensity. Moreover, this metric is able to identify the set of nodes which are more vulnerable under an epidemic attack. In addition, two applications of epidemic survivability are provided. First, we introduce epidemic criticality, a novel robustness metric for epidemic failure scenarios. A case study shows the utility of this new metric comparing several network topologies and epidemic intensities. Then, two immunization strategies are proposed: high epidemic survivability (HES) and high epidemic survivability adaptive (HESA). The presented results show that network vulnerability can be significantly reduced by using our proposals, compared to other well-known existing methods.
Survivability
Vulnerability
Epidemic model
Robustness
Cascading failure
Resilience
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Control of COVID-19 Pandemic: Vaccination Strategies Simulation under Probabilistic Node-Level Model
This paper aims at constructing a probabilistic node-level time-dependent contagious disease spreading model for coronavirus disease (COVID-19) pandemic which is called SEINRVseinr by introducing exposed and asymptomatic infectious state, imperfect vaccination, reinfected possibility and weighted undirected graph for social network into the traditional probabilistic node-level Susceptible-Infectious-Recovered (SIR) network model. This paper simulates the effectiveness of five vaccination strategies (including random base, degree-target base, random acquaintance, first-neighbor and second neighbor strategies) in random network, small world network and scale-free network. Compared with the benchmark model, the results show that random acquaintance strategy is efficient strategy and neighbors' strategies perform better in certain interval.
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