컨텐츠 시작
학술대회/행사
초록검색
| 제출번호(No.) | 0142 |
|---|---|
| 분류(Section) | Special Session |
| 분과(Session) | (SS-21) Recent Progress in Mathematical Biology (SS-21) |
| 발표시간(Time) | 19th-B-13:50 -- 14:10 |
| 영문제목 (Title(Eng.)) |
Forecasting seasonal infectious disease outbreaks and analyzing the impact of the COVID-19 |
| 저자(Author(s)) |
Geunsoo Jang1, Hyojung Lee2, Jeonghwa Seo2 NDMAC at Kyungpook National University1, Kyungpook National University2 |
| 초록본문(Abstract) | Seasonal infectious diseases, including norovirus and influenza, are manageable through stringent personal hygiene practices and prompt isolation during outbreaks. The prediction and detection of outbreaks serve as a key for curbing disease transmission. This study aims to predict the case of influenza, norovirus, tsutsugamushi, and severe fever with thrombocytopenia syndrome (SFTS) while analyzing the overarching impact of the coronavirus pandemic on these diseases. To achieve this, we employed the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model, long short-term memory (LSTM), and a hybird model that combine SARIMAX and LSTM. We also applied change point method for early detection. This comprehensive approach not only facilitates the accurate prediction of disease outbreaks but also allows for an analysis of the impact each disease may have in the context of the coronavirus pandemic. |
| 분류기호 (MSC number(s)) |
92B05 |
| 키워드(Keyword(s)) | Seasonal infectious disease, hybrid model, COVID-19 |
| 강연 형태 (Language of Session (Talk)) |
Korean |