BACKGROUND: With a tropical rainforest climate, rapid urbanization, changing demography and ecology, Singapore experiences endemic dengue, with the last large outbreak in 2013 culminating in 22170 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 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 three-month time horizon. This forecasting tool makes use of a variety of data streams, updatable weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared to 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 three month lag using the LASSO-derived-models. Based on mean average percentage error, the LASSO approach provided more accurate forecasts than the previously-published 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.
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