• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.163-171
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• 2014

Epidemic models are used to analyze the spreading of epidemic diseases, estimate public health needs, and assess the effectiveness of mitigation strategies. Modeling scope of an epidemic model ranges from the regional scale to national and global scale. Most of the epidemic models developed in Korea are at the national scale using the equation-based model. While these models are useful for designing and evaluating national public health policies, they do not provide sufficient details. As an alternative, individual-based models at the regional scale are often used to describe disease spreading, so that various mitigation strategies can be designed and tested. This paper presents an individual-based epidemic spreading model at regional scale. This model incorporates 2005 census data to build the synthetic population in the model representing Daejeon in 2005. The model's capability is demonstrated by an example where we assess the effectiveness of several mitigation strategies using the model.

• Communications of the Korean Mathematical Society
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• v.26 no.2
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• pp.321-338
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• 2011

In this paper, a class of delayed epidemic model with diffusion is investigated. By analyzing the associated characteristic transcendental equation, its linear stability is investigated and Hopf bifurcation is demonstrated. Some explicit formulae determining the stability and the direction of the Hopf bifurcation periodic solutions bifurcating from Hopf bifurcations are obtained by using the normal form theory and center manifold theory. Some numerical simulation are also carried out to support our analytical findings. Finally, biological explanations and main conclusions are given.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.656-666
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• 2014

In delay tolerant networks (DTNs), delay is inevitable; thus, making better use of buffer space to maximize the packet delivery rate is more important than delay reduction. In DTNs, epidemic routing is a well-known routing protocol. However, epidemic routing is very sensitive to buffer size. Once the buffer size in nodes is insufficient, the performance of epidemic routing will be drastically reduced. In this paper, we propose a buffer scheme to optimize the performance of epidemic routing on the basis of the Lagrangian and dual problem models. By using the proposed optimal buffer scheme, the packet delivery rate in epidemic routing is considerably improved. Our simulation results show that epidemic routing with the proposed optimal buffer scheme outperforms the original epidemic routing in terms of packet delivery rate and average end-to-end delay. It is worth noting that the improved epidemic routing needs much less buffer size compared to that of the original epidemic routing for ensuring the same packet delivery rate. In particular, even though the buffer size is very small (e.g., 50), the packet delivery rate in epidemic routing with the proposed optimal buffer scheme is still 95.8%, which can satisfy general communication demand.

• Korean System Dynamics Review
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• v.14 no.4
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• pp.37-62
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• 2013

The purpose of this paper is to find out some clues about effectiveness and implementing timing of the control measures against Foot-and-Mouth Disease (FMD) in Korea. To do that, the case of FMD, which broke out during November 2010 in Korea, is examined and constructed as a system dynamics simulation model. The implications of simulation result are as follows. First of all, it is most effective measure to prevent the movement of vehicle from infected farmhouse to other noninfected farmhouses, which are located far away, in the early stage of FMD diffusion. Secondly, earlier vaccination can be adopted as a reliable means to control the epidemic when FMD spreads widely. Finally, reducing the time to vaccinate the whole noninfected livestock is a little help to restrain the additional infections of FMD.

• Journal of Computing Science and Engineering
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• v.2 no.3
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• pp.249-273
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• 2008

Epidemic protocols have two fundamental assumptions. One is the availability of a mechanism that provides each node with a set of log(N) (fanout) nodes to gossip with at each cycle. The other is that the network size N is known to all member nodes. While it may be trivial to support these assumptions in small systems, it is a challenge to realize them in large open dynamic systems, such as peer-to-peer (P2P) systems. Technically, since the most fundamental parameter of epidemic protocols is log(N), without knowing the system size, the protocols will be limited. Further, since the network churn, frequently observed in P2P systems, causes rapid membership changes, providing a different set of log(N) at each cycle is a difficult problem. In order to support the assumptions, the fanout nodes should be selected randomly and uniformly from the entire membership. This paper investigates one possible solution which addresses both problems; providing at each cycle a different set of log(N) nodes selected randomly and uniformly from the entire network under churn, and estimating the dynamic network size in the number of nodes. This solution improves the previously developed distributed algorithm called Shuffle to deal with churn, and utilizes the Shuffle infrastructure to estimate the dynamic network size. The effectiveness of the proposed solution is evaluated by simulation. According to the simulation results, the proposed algorithms successfully handle network churn in providing random log(N0 fanout nodes, and practically and accurately estimate the network size. Overall, this work provides insights in designing epidemic protocols for large scale open dynamic systems, where the protocols behave autonomically.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.259-265
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• 2018

Compartment models, a type of mathematical model, have been widely applied to characterize the changes in a dynamic system with sequential events or processes, such as the spread of an epidemic disease. A compartment model comprises compartments, and the relations between compartments are depicted as boxes and arrows. This principle is similar to that of the system dynamics (SD) approach to constructing a simulation model with stocks and flows. In addition, both models are structured using differential equations. With this mutual and translatable principle, this study, in terms of SD, translates a reference SEIR model, which was developed in a recent study to characterize the transmission of the Middle East respiratory syndrome (MERS) in South Korea. Compared to the replicated result of the reference SEIR model (Model 1), the translated SEIR model (Model 2) demonstrates the same simulation result (error=0). The results of this study provide insight into the application of SD relative to constructing an epidemic compartment model using schematization and differential equations. The translated SD artifact can be used as a reference model for other epidemic diseases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2020-2037
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• 2017

In order to improve the energy efficiency of n-Epidemic routing protocol in opportunistic networks, in which a stable end-to-end forwarding path usually does not exist, a novel adjustment strategy for parameter n is proposed using learning atuomata principle. First, nodes dynamically update the average energy level of current environment while moving around. Second, nodes with lower energy level relative to their neighbors take larger n avoiding energy consumption during message replications and vice versa. Third, nodes will only replicate messages to their neighbors when the number of neighbors reaches or exceeds the threshold n. Thus the number of message transmissions is reduced and energy is conserved accordingly. The simulation results show that, n-Epidemic routing protocol with the proposed adjustment method can efficiently reduce and balance energy consumption. Furthermore, the key metric of delivery ratio is improved compared with the original n-Epidemic routing protocol. Obviously the proposed scheme prolongs the network life time because of the equilibrium of energy consumption among nodes.

• 대한예방의학회:학술대회논문집
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• 2005.10a
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• pp.489-489
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• 2005

• The Pure and Applied Mathematics
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• v.31 no.2
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• pp.131-158
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• 2024

This study investigates the impact of precautionary measures, such as isolating exposed individuals, wearing masks, and maintaining physical distance, on preventing infectious disease. A deterministic SEIQHRV epidemic model is employed for this purpose. The model's positivity, boundedness, disease-free, and endemic equilibrium points are identified. A sensitivity test assesses the impact of preventive measures on infected classes. Results show that a basic reproduction number less than unity drives disease eradiction, while a higher unity value encourages the adoption of preventive measures.

• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.65-73
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• 2018

In this paper, we propose a simulation output analysis environment using Elastic Stack technology in order to reduce the complexity of the simulation analysis process. The proposed simulation output analysis environment automatically transfers simulation outputs to a centralized analysis server from a set of simulation execution resources, physically separated over a network, manages the collected simulation outputs in a fashion that further analysis tasks can be easily performed, and provides a connection to analysis and visualization services of Kibana in Elastic Stack. The proposed analysis environment provides scalability where a set of computation resources can be added on demand. We demonstrate how the proposed simulation output analysis environment can perform the simulation output analysis effectively with an example of spreading epidemic diseases, such as influenza and flu.