인터넷 검색어를 활용한 계절적인 유행성 독감 발생 감지

Monitoring Seasonal Influenza Epidemics in Korea through Query Search

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.