인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
Norovirus disease in the Republic of Korea consistently displays a seasonal pattern, especially prominent during the winter months. Although the timing of outbreak periods can change due to climate change, these outbreaks continue to occur regularly. It is crucial to analyze these seasonal patterns to predict the occurrence of the outbreak. Notably, no quantitative criteria have been established to determine the onset of norovirus outbreaks.
In this study, we aim to establish criteria for predicting the occurrence of norovirus outbreaks using various machine learning classification methods, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM). By utilizing meteorological data, past incidence data, and other variables, we predict norovirus outbreak periods based on the criteria suggested in the present study. The training period spans from 2016 to 2022, while the 2023 data is used for testing. We compare the prediction accuracy of outbreak periods based on the selected variables.
Our results indicate that among the machine learning methods tested, SVM and RF achieved the highest F1-scores in classifying outbreak periods. Additionally, the models accurately predicted the first thirteen weeks of 2023 as an outbreak period. By accurately predicting the occurrence of norovirus outbreaks, our approach enables the early detection of an increase in the number of norovirus cases, allowing for proactive measures and prevention strategies to mitigate the impact of these outbreaks.
In this study, we aim to establish criteria for predicting the occurrence of norovirus outbreaks using various machine learning classification methods, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM). By utilizing meteorological data, past incidence data, and other variables, we predict norovirus outbreak periods based on the criteria suggested in the present study. The training period spans from 2016 to 2022, while the 2023 data is used for testing. We compare the prediction accuracy of outbreak periods based on the selected variables.
Our results indicate that among the machine learning methods tested, SVM and RF achieved the highest F1-scores in classifying outbreak periods. Additionally, the models accurately predicted the first thirteen weeks of 2023 as an outbreak period. By accurately predicting the occurrence of norovirus outbreaks, our approach enables the early detection of an increase in the number of norovirus cases, allowing for proactive measures and prevention strategies to mitigate the impact of these outbreaks.
본문·목차
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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