Dengue forecasting in Sao Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models.

ABSTRACT: Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city-São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times high

SUBMITTER: Baquero OS 

PROVIDER: S-EPMC5880372 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature