A study of epidemic model using SEIR model
Mijin Do1 · Jongtae Kim2 · Boseung Choi3
1Department of Statistics, Daegu University
2Department of Computer Science and Statistics, Daegu University
3Department of Applied Statistics, Korea University
2Department of Computer Science and Statistics, Daegu University
3Department of Applied Statistics, Korea University
Correspondence to: Boseung Choi
Assistant professor, Department of Applied Statistics, Korea University Sejong Campus, Sejong 30019, Korea. E-mail: cbskust@korea.ac.kr
Assistant professor, Department of Applied Statistics, Korea University Sejong Campus, Sejong 30019, Korea. E-mail: cbskust@korea.ac.kr
Received February 10, 2017; Revised March 7, 2017; Accepted March 8, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- 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.
- Keywords : Epidemic model, MERS, SEIR model, Stochastic model.
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