1. Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore
- Author
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Kim-Sung Lee, Sharon S.Y. Tan, Alex R. Cook, Waiming Kong, Yuan Shi, Grace Yap, Suet-Yheng Kok, Andrew Lo, Chee-Seng Chong, Shaohong Liang, Xu Liu, Jayanthi Rajarethinam, Lee Ching Ng, and Christopher Kuan Yew Chin
- Subjects
0301 basic medicine ,Time Factors ,Health, Toxicology and Mutagenesis ,030231 tropical medicine ,Dengue fever ,Decision Support Techniques ,Disease Outbreaks ,Dengue ,03 medical and health sciences ,0302 clinical medicine ,Policy decision ,Urbanization ,Statistics ,Medicine ,Humans ,Socioeconomics ,Health policy ,News | Science Selections ,Singapore ,Models, Statistical ,business.industry ,Health Policy ,Incidence ,Public Health, Environmental and Occupational Health ,Outbreak ,medicine.disease ,030104 developmental biology ,Early warning system ,sense organs ,Public Health ,business ,Tropical rainforest ,Forecasting - Abstract
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.We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak.We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore.Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response.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.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. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369-1375; http://dx.doi.org/10.1289/ehp.1509981.
- Published
- 2015