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Stochastic epidemic model estimation using Monte Carlo expection maximization algorithm
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몬테 카를로 expectation maximization 방법을 이용한 확률적 질병 확산 모형 추정 연구

논문 기본 정보

Type
Academic journal
Author
Boseung Choi (고려대학교) Yong Hwa Yoon (대구대학교)
Journal
The Korean Data and Information Science Society Journal of the Korean Data And Information Science Society Vol.29 No.1 KCI Excellent Accredited Journal
Published
2018.1
Pages
97 - 109 (13page)
DOI
10.7465/jkdi.2018.29.1.97

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Stochastic epidemic model estimation using Monte Carlo expection maximization algorithm
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Abstract· Keywords

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In this paper, we introduce a statistical method for modeling the spread of disease. Historically, the ordinary differential equation is proposed to construct the epidemic model. However, the deterministic approach for the epidemic model is too simplified to capture the stochastic behavior of spread of disease. We consider the stochastic kinetic networks model for the epidemic modeling and we proposed MCEM (Monte Carlo expectation maximization) method to perform the statistical inference for reaction constants of the stochastic epidemic model. We applied our MCEM method to a synthetic data from the representative stochastic epidemic model, named SIRS (susceptible - infected - recovered - susceptible) model and we compared proposed MCEM method with two Bayesian MCMC methods. The MCEM result gives stable and faster convergence results. We also MCEM method to the data from the onset of early pandemic of H1N1 in the US. The proposed MCEM method can be an alternative to the estimation method for the stochastic epidemic model.

Contents

요약
1. 서론
2. 통계적 모형 추정 방법
3. 자료 분석
4. 결론
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UCI(KEPA) : I410-ECN-0101-2018-041-001766141