Spatial Distribution Analysis and Comparative Forecasting of Dengue Resurgence in the Philippines (2025-2027): A Nationwide Study.

ABSTRACT: Prediction of dengue continues to be valuable in endemic countries. Time series forecasting methods have been widely employed for predicting future dengue trends and outbreaks. The study aimed to determine the spatial distribution, trends, and seasonality of dengue cases and compare the predictive accuracy of seasonal autoregressive integrated moving average (SARIMA), neural network autoregression (NNAR), random forest (RF), long-short term memory (LSTM), trigonometric exponential smoothing state-space model with Box-Cox transformation, ARMA errors, trend and seasonal components (TBATS), and Prophet in forecasting dengue cases in the Philippines. Monthly data from 2017 to 2024 across all provinces were obtained and were partitioned into training (January 2017-December 2023) and testing seg

SUBMITTER: Olana KOA 

PROVIDER: S-EPMC12543447 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature