Department of Artificial Intelligence and Software, Kangwon National University, Samcheok-si, Republic of Korea.
Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea.
Front Public Health. 2023 Dec 18;11:1252357. doi: 10.3389/fpubh.2023.1252357. eCollection 2023.
The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.
In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.
We developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.
ML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.
The study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.
冠状病毒病（COVID-19）疫情在全球迅速蔓延，因此迫切需要能够帮助医疗保健提供者更快、更有效地准备和应对疫情爆发的预测模型，最终改善患者护理。早期检测和预警系统对于预防和控制疫情传播至关重要。
本研究旨在提出一种基于机器学习的方法，通过分析流行病学数据来预测 COVID-19 的传播趋势，并提出一种新的方法来检测新疫情的开始时间。
我们开发了一个风险指数来衡量传播趋势的变化。我们应用机器学习（ML）技术来预测 COVID-19 的传播趋势，分为三个标签：减少（L0）、维持（L1）和增加（L2）。我们使用支持向量机（SVM）、随机森林（RF）和 XGBoost（XGB）作为 ML 模型。我们采用网格搜索方法来确定这三个模型的最佳超参数。我们提出了一种基于标签 2 的新方法来检测新疫情的开始时间，该标签至少持续 14 天（即维持时间）。我们比较了不同 ML 模型的性能，以确定最准确的疫情检测方法。我们对维持时间在 7 天到 28 天之间进行了敏感性分析。
ML 方法在估计传播趋势的分类方面表现出很高的准确性（超过 94%）。我们提出的方法成功预测了新疫情的开始时间，总共可以检测到七个估计的疫情，而在 2020 年 3 月至 2022 年 10 月期间，韩国共报告了五个疫情。这意味着我们的方法可以检测到轻微的疫情。在 ML 模型中，RF 和 XGB 分类器在疫情检测方面表现出最高的准确性。
本研究强调了我们的方法在使用可解释和可解释的方法准确预测疫情开始时间方面的优势。它可以为预测新疫情的开始时间和检测未来的传播趋势提供一个标准。这种方法可以为大流行期间的有针对性的预防和控制措施的制定以及资源管理做出贡献。
