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[학술대회자료]

  • 학술대회자료

정재운(동아대학교) 황성원(동아대학교) 권치명(동아대학교)

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초록

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 25,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop a linear regression model for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver(Korean representative portal site) trend service. Some issues related to selection of key words, model building and its feasibility to real data will be discussed.

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Abstract
1. 서론
2. 유행성 독감 임상자료 수집
3. 검색 키워드 선택과 정제(filtering)
4. 감지 모형 및 결과분석
5. 결론 및 토의
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