• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1487-1493
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• 2016

The susceptible, exposed, infectious, and recovered model (SEIR) is used extensively in the field of epidemiology. On the other hand, dissemination information among users through internet grows exponentially. This information spreading can be modeled as an epidemic. In this paper, we derive the mathematical model of SEIR in viral communication from the view of optimal control theory. Overall the methods based on classical calculus, In order to solve the optimal control problem, proved to be more efficient and accurate. According to Pontryagin's minimum principle (PMP) the Hamiltonian function must be optimized by the control variables at all points along the solution trajectory. We present our method based on the PMP and forward backward algorithm. In this algorithm, one should integrate forward in time for the state equations then integrate backward in time for the adjoint equations resulting from the optimality conditions. The problem is mathematically analyzed and numerically solved as well.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.297-307
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• 2017

The epidemic model is used to model the spread of disease and to control the disease. In this research, we utilize SEIR model which is one of applications the SIR model that incorporates Exposed step to the model. The SEIR model assumes that a people in the susceptible contacted infected moves to the exposed period. After staying in the period, the infectee tends to sequentially proceed to the status of infected, recovered, and removed. This type of infection can be used for research in cases where there is a latency period after infectious disease. In this research, we collected respiratory infectious disease data for the Middle East Respiratory Syndrome Coronavirus (MERSCoV). Assuming that the spread of disease follows a stochastic process rather than a deterministic one, we utilized the Poisson process for the variation of infection and applied epidemic model to the stochastic chemical reaction model. Using observed pandemic data, we estimated three parameters in the SIER model; exposed rate, transmission rate, and recovery rate. After estimating the model, we applied the fitted model to the explanation of spread disease. Additionally, we include a process for generating the Exposed trajectory during the model estimation process due to the lack of the information of exact trajectory of Exposed.

• Bulletin of the Korean Mathematical Society
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• v.49 no.3
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• pp.517-528
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• 2012

This paper is estimating the domain of attraction for a class of susceptible-exposed-infectious-recovered (SEIR) epidemic dynamic models by using sum of squares optimization. First, the stability is analyzed for the equilibriums of SEIR model, and the domain of attraction in the endemic equilibrium is estimated by using sum of squares optimization. Finally, a numerical example is examined.

• Communications of Mathematical Education
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• v.24 no.4
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• pp.877-889
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• 2010

Mathematical modelling is a useful method for reinterpreting the real world and for solving real problems. In this paper, we introduced a theory on mathematical modelling. Further, we developed a mathematical model of the H1N1 influenza with Excel. Then, we analyzed the model which tells us what role it can play in an appropriate prediction of the future and in the decision of accompanied policies.

• Journal of applied mathematics & informatics
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• v.41 no.5
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• pp.1017-1035
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• 2023

In this paper, we construct a monkeypox model which is similar to smallpox infection. It is caused by a monkeypox virus which is related to Poxviridae family. It will occur mostly in West African communities and in remote Central. We develop a system of differential equations for an SEIR (Suspected, Exposed, Infected and Recovered) model and analyze the outbreak of monkeypox disease and its effect on United States(US) population. We establish theorems on asymptotical stability conditions for endemic equilibrium and disease-free equilibrium. The basic reproduction number R₀ has been determined using next generation matrix. We expect that this study will be effective at controlling monkeypox spread in United States. Our goal is to see whether monkeypox can be controlled and destroyed by smallpox vaccination. We find that monkeypox is controllable and can be fully destroyed in disease free state by vaccination. However, in the endemic state, monkeypox cannot be destroyed by vaccination alone.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Journal for History of Mathematics
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• v.26 no.2_3
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• pp.197-210
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• 2013

The late 18C Malthus studied population growth for the first time, Verhulst the logistic model in 19C and, after that, the study of the predation competition between two species resulted in the appearance of Lotka-Volterra model and modified model supported by Gause's experiment with bacteria. Instable coexistence equilibrium being found, Solomon and Holling proposed functional and numerical response considering limited abilities of predator on prey, which applied to Lotka Volterra model. Nicholson and Baily, considering the predation between host and parasitoid in discrete time, made a model. In 20C there were developed various models of disease dynamics with the help of mathematics and real data and named SIS, SIR or SEIR on the basis of dynamical phenomena.

• Journal of the military operations research society of Korea
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• v.29 no.2
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• pp.26-44
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• 2003

This study Is to forecast the damage of smallpox as a biological weapon and to measure the effect of potential responses (quarantine, vaccination and cure) to the spread of smallpox infection when a smallpox bioterrorism attack occurs. We designed the smallpox spreading simulation model through the literature study on a basis of some existing infectious disease models such as SIR, SEIR model by using Vensim program. In order to evaluate the performance of responses to smallpox, we measure the total infection population, infection sustaining duration, average infection rate and the infection spreading behavior of the smallpox. This study can help those who are related to the bioterrorism forecast the present and possible demage, and take more effective actions for minimizing the damage by smallpox bioterrorism.

• East Asian mathematical journal
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• v.39 no.5
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• pp.493-503
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• 2023

Tendency prediction of daily confirmed cases is an important issue for public health authorities. To protect the tendency, we estimate the transmission rate of stochastic SEIR model for COVID-19 in Korea using particle Markov chain Monte Carlo method. The results show that the increasing and decreasing tendency of estimated transmission rate appear one or two days in advance compared to daily incidence cases, and as time evolves the standard deviation of the estimates of transmission rate reduces. Since ten months have passed since the first incident case of COVID-19 in Korea, we expect to forecast the tendency of daily confirmed cases for the next one or two days more accurately using our method.