Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects.

ABSTRACT:

Background

Dengue fever is a mosquito-borne viral disease that poses significant health risks and socioeconomic challenges in Brazil, necessitating accurate forecasting across its 27 federal states. With the country's diverse climate and geographical spread, effective dengue prediction requires models that can account for both climate variations and spatial dynamics. This study addresses these needs by using Long Short-Term Memory (LSTM) neural networks enhanced with SHapley Additive exPlanations (SHAP) integrating optimal lagged climate variables and spatial influence from neighboring states.

Method

An LSTM-based model was developed to forecast dengue cases across Brazil's 27 federal states, incorporating a comprehensive set of climate and spatial variables. SHAP was used t

SUBMITTER: Chen X 

PROVIDER: S-EPMC11900637 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature