Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore.

ABSTRACT:

Background

Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contrib

SUBMITTER: Benedum CM 

PROVIDER: S-EPMC7567393 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature