[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123736-en":3,"doc-seo-123736-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123736,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Detection of COVID-19 epidemic outbreak using machine learning - Research article","COVID-19 continued to spread globally, creating an urgent need for predictive analytics that help healthcare providers recognize outbreaks early and respond with greater speed and effectiveness. This study develops a machine learning framework that predicts COVID-19 transmission trends and detects the start time of new outbreaks from epidemiological data. A risk index measures trend changes, while SVM, Random Forest, and XGBoost classify transmission into decrease, maintain, or increase labels, supporting high-accuracy outbreak timing detection and improved resource management.","TYPE Original Research PUBLISHED 18 December 2023 DOI 10.3389/fpubh.2023.1252357  \nOPEN ACCESS  \nEDITED BY  \nFathiah Zakham,  \nUniversity of Helsinki, Finland  \nREVIEWED BY  \nJunxiang Chen,  \nIndiana University, United States Ana Clara Gomes da Silva, Universidade de Pernambuco, Brazil Dinh Tuan Phan Le,  \nNew York City Health and Hospitals Corporation, United States  \n*CORRESPONDENCE  \nHyojung Lee  \n hj[lee@knu.ac.kr](lee@knu.ac.kr)  \n†These authors have contributed equally to this work  \nRECEIVED 03 July 2023  \nACCEPTED 01 December 2023  \nPUBLISHED 18 December 2023  \nCITATION  \nCho G, Park JR, Choi Y, Ahn H and  \nLee H (2023) Detection of COVID-19 epidemic outbreak using machine learning.  \nFront. Public Health 11:1252357.  \ndoi: 10.3389/fpubh.2023.1252357  \nCOPYRIGHT  \n© 2023 Cho, Park, Choi, Ahn and Lee. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDetection of COVID-19 epidemic outbreak using machine learning  \nGiphil Cho 1†, Jeong Rye Park 2†, Yongin Choi3, Hyeonjeong Ahn4 and Hyojung Lee4*  \n1 Department of Artificial Intelligence and Software, Kangwon National University, Samcheok-si, Republic of Korea, 2 Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea, 3 Busan Center for Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea, 4 Department of Statistics, Kyungpook National University, Daegu, Republic of Korea  \nBackground: 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.  \nObjective: 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.  \nMethods: 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 anew 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.  \nResults: 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.  \nConclusion: 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 trend","cbCaivi5JjTROutO","https://ap.wps.com/l/cbCaivi5JjTROutO","pdf",2249238,1,12,"English","en",105,"# 1 Introduction\n# 2 Background and Motivation\n# 3 Objective\n# 4 Methods\n## Risk index and label-based trend modeling\n## ML models and hyperparameter tuning\n## Outbreak start-time detection approach\n## Sensitivity analysis\n# 5 Results\n# 6 Conclusion","[{\"question\":\"What is the study’s main goal in detecting COVID-19 outbreaks?\",\"answer\":\"To propose a machine learning method that predicts COVID-19 transmission trends and detects the start time of new outbreaks by analyzing epidemiological data.\"},{\"question\":\"How are transmission trends categorized in the proposed method?\",\"answer\":\"Transmission trends are categorized into three labels: decrease (L0), maintain (L1), and increase (L2).\"},{\"question\":\"Which machine learning models performed best for outbreak detection?\",\"answer\":\"Among SVM, Random Forest, and XGBoost, Random Forest and XGBoost achieved the highest accuracy for outbreak detection.\"}]","Detection of COVID-19 epidemic outbreak using machine learning - 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