Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore | Zendy

Yuan Shi | Zendy; Xu Liu | Zendy; Suet-Yheng Kok | Zendy; Jayanthi Rajarethinam | Zendy; Shaohong Liang | Zendy; Grace Yap | Zendy; CheeSeng Chong | Zendy; Kim-Sung Lee | Zendy; Sharon S.Y. Tan | Zendy; Christopher Kuan Yew Chin | Zendy; Andrew W. Lo | Zendy; Waiming Kong | Zendy; Lee Ching Ng | Zendy; Alex R. Cook | Zendy

Open Access

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

Author(s) -

Yuan Shi,

Xu Liu,

Suet-Yheng Kok,

Jayanthi Rajarethinam,

Shaohong Liang,

Grace Yap,

CheeSeng Chong,

Kim-Sung Lee,

Sharon S.Y. Tan,

Christopher Kuan Yew Chin,

Andrew W. Lo,

Waiming Kong,

Lee Ching Ng,

Alex R. Cook

Publication year - 2015

Publication title -

environmental health perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.257

H-Index - 282

eISSN - 1552-9924

pISSN - 0091-6765

DOI - 10.1289/ehp.1509981

Subject(s) - dengue fever , outbreak , geography , lasso (programming language) , early warning system , warning system , autoregressive integrated moving average , population , statistics , computer science , time series , econometrics , machine learning , medicine , environmental health , mathematics , virology , telecommunications , world wide web

With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention.

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