East Asian mathematical journal

The Youngnam Mathematical Society (영남수학회)
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EARLY WARNING FORECASTS FOR COVID-19 IN KOREA USING BAYESIAN ESTIMATION OF THE TRANSMISSION RATE

  • Byul Nim Kim (Institute for Mathematical Convergence, Kyungpook National University, Finance Fishery Manufacture Industrial Mathematics Center on Big Data, Busan National University)
  • Received : 2023.08.14
  • Accepted : 2023.08.30
  • Published : 2023.09.30
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Abstract

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.

Keywords

Acknowledgement

This research was supported by Kyungpook National University Development Project Research Fund, 2020

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