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Forecasting of the COVID-19 pandemic situation of Korea

Genomics & Informatics 2021년 19권 1호 p.11 ~ 11
구태완, Apio Catherine, 허규진, 이도은, 이종혁, 임지선, 한결희, 박태성,
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구태완 ( Goo Tae-Wan ) - Seoul National University Interdisciplinary Program in Bioinformatics
 ( Apio Catherine ) - Seoul National University Interdisciplinary Program in Bioinformatics
허규진 ( Heo Gyu-Jin ) - Seoul National University Interdisciplinary Program in Bioinformatics
이도은 ( Lee Do-Eun ) - Seoul National University Interdisciplinary Program in Bioinformatics
이종혁 ( Lee Jong-Hyeok ) - Seoul National University Department of Statistics
임지선 ( Lim Ji-Sun ) - Seoul National University Research Institute of Basic Sciences
한결희 ( Han Kyul-Hee ) - Seoul National University Interdisciplinary Program in Bioinformatics
박태성 ( Park Tae-Sung ) - Seoul National University Interdisciplinary Program in Bioinformatics

Abstract


For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/susceptible infected recoverd (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.

키워드

COVID-19; deep learning; disease transmission; mathematical model; pandemics; statistical model

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