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

BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: 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. 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. 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. 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.

Entomologic and virologic investigation of Chikungunya, Singapore.

Local transmission of chikungunya, a debilitating mosquito-borne viral disease, was first reported in Singapore in January 2008. After 3 months of absence, locally acquired Chikungunya cases resurfaced in May 2008, causing an outbreak that resulted in a total of 231 cases by September 2008. The circulating viruses were related to East, Central, and South African genotypes that emerged in the Indian Ocean region in 2005. The first local outbreak was due to a wild-type virus (alanine at codon 226 of the envelope 1 gene) and occurred in an area where Aedes aegypti mosquitoes were the primary vector. Strains isolated during subsequent outbreaks showed alanine to valine substitution (A226V) and largely spread in areas predominated by Ae. albopictus mosquitoes. These findings led to a revision of the current vector control strategy in Singapore. This report highlights the use of entomologic and virologic data to assist in the control of chikungunya in disease-endemic areas.