DTMC-Based Epidemic Model for Hidden Patients Estimation: Using COVID-19 Dataset of the Republic of Korea 


Vol. 47,  No. 10, pp. 1586-1597, Oct.  2022
10.7840/kics.2022.47.10.1586


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  Abstract

Recently, the importance of mathematical modeling for infectious diseases has been highlighted. It can investigate the transmission by capturing the ongoing pattern of infectious diseases. This paper proposes a data-driven approach based on discrete-time Markov chain model for the epidemic by estimating the model's parameters and latent state values. We introduce the key model parameters such as transmission rate as time-varying so that the parameters can quantitatively evaluate the effectiveness of the national prevention policies. Furthermore, our epidemic model contains two additional states complying with national intervention: vaccinated and isolated states. To verify the proposed model, we use the COVID-19 dataset of the Republic of Korea and analyze the data by using estimated parameters and the number of people in the latent states. Through the results, we confirm that the proposed solution performs the best by capturing important details.

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[IEEE Style]

S. Ahn and M. Kwon, "DTMC-Based Epidemic Model for Hidden Patients Estimation: Using COVID-19 Dataset of the Republic of Korea," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 10, pp. 1586-1597, 2022. DOI: 10.7840/kics.2022.47.10.1586.

[ACM Style]

Sujin Ahn and Minhae Kwon. 2022. DTMC-Based Epidemic Model for Hidden Patients Estimation: Using COVID-19 Dataset of the Republic of Korea. The Journal of Korean Institute of Communications and Information Sciences, 47, 10, (2022), 1586-1597. DOI: 10.7840/kics.2022.47.10.1586.

[KICS Style]

Sujin Ahn and Minhae Kwon, "DTMC-Based Epidemic Model for Hidden Patients Estimation: Using COVID-19 Dataset of the Republic of Korea," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 10, pp. 1586-1597, 10. 2022. (https://doi.org/10.7840/kics.2022.47.10.1586)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880