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

Conclusions: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. This EHP Advance Publication article has been peer-reviewed, revised, and accepted for publication. EHP Advance Publication articles are completely citable using the DOI number assigned to the article. This document will be replaced with the copyedited and formatted version as soon as it is available. Through the DOI number used in the citation, you will be able to access this document at each stage of the publication process. Citation: Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore. Environ Health Perspect; http://dx.doi.org/10.1289/ehp.1509981 Received: 20 March 2015 Accepted: 24 November 2015 Advance Publication: 11 December 2015 Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact ehp508@nieh...
Source: EHP Research - Category: Environmental Health Authors: Tags: Research Article Source Type: research