Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases

Hyeonjeong Ahn; Hyojung Lee; Hyeonjeong Ahn; Hyojung Lee

doi:10.3934/mbe.2024270

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 5: 6150-6166. doi: 10.3934/mbe.2024270

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Research article

Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases

Hyeonjeong Ahn ,
Hyojung Lee ^,

Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea

Academic Editor: Xi Huo

Received: 14 January 2024 Revised: 21 May 2024 Accepted: 29 May 2024 Published: 03 June 2024

COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control. This study aims to predict the transmission patterns of COVID-19 using machine learning, such as support vector machine, random forest, and XGBoost, using confirmed cases, death cases, and imported cases, respectively. The study categorizes the transmission trends into the three groups: L0 (decrease), L1 (maintain), and L2 (increase). We develop the risk index function to quantify changes in the transmission trends, which is applied to the classification of machine learning. A high accuracy is achieved when estimating the transmission trends for the confirmed cases (91.5–95.5%), death cases (85.6–91.8%), and imported cases (77.7–89.4%). Notably, the confirmed cases exhibit a higher level of accuracy compared to the data on the deaths and imported cases. L2 predictions outperformed L0 and L1 in all cases. Predicting L2 is important because it can lead to new outbreaks. Thus, this robust L2 prediction is crucial for the timely implementation of control policies for the management of transmission dynamics.
- COVID-19,
- machine learning,
- transmission,
- classification,
- prediction
Citation: Hyeonjeong Ahn, Hyojung Lee. Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases[J]. Mathematical Biosciences and Engineering, 2024, 21(5): 6150-6166. doi: 10.3934/mbe.2024270

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Abstract

COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control. This study aims to predict the transmission patterns of COVID-19 using machine learning, such as support vector machine, random forest, and XGBoost, using confirmed cases, death cases, and imported cases, respectively. The study categorizes the transmission trends into the three groups: L0 (decrease), L1 (maintain), and L2 (increase). We develop the risk index function to quantify changes in the transmission trends, which is applied to the classification of machine learning. A high accuracy is achieved when estimating the transmission trends for the confirmed cases (91.5–95.5%), death cases (85.6–91.8%), and imported cases (77.7–89.4%). Notably, the confirmed cases exhibit a higher level of accuracy compared to the data on the deaths and imported cases. L2 predictions outperformed L0 and L1 in all cases. Predicting L2 is important because it can lead to new outbreaks. Thus, this robust L2 prediction is crucial for the timely implementation of control policies for the management of transmission dynamics.

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