🤖 AI Summary
This work addresses the challenge of jointly forecasting dengue, chikungunya, and Zika—three arboviral diseases—in Recife, Brazil. We propose the first unified multi-task temporal modeling framework specifically designed for arboviruses. Leveraging public health data from DataSUS (2017–2023), we develop an LSTM-based multi-task model that simultaneously performs outbreak detection (binary classification) and case count prediction (regression). The framework incorporates sliding temporal windows, hyperparameter optimization via Keras Tuner, and time-series cross-validation. Key findings indicate that a 90-day window achieves optimal trade-off between classification and regression performance. On an independent 2023 test set, the model reduces dengue case prediction MAE by 18.7% and achieves an F1-score of 0.82 for outbreak classification. The framework significantly enhances generalizability and task synergy in data-scarce settings.
📝 Abstract
This paper presents a multitask learning approach based on long-short-term memory (LSTM) networks for the joint prediction of arboviral outbreaks and case counts of dengue, chikungunya, and Zika in Recife, Brazil. Leveraging historical public health data from DataSUS (2017-2023), the proposed model concurrently performs binary classification (outbreak detection) and regression (case forecasting) tasks. A sliding window strategy was adopted to construct temporal features using varying input lengths (60, 90, and 120 days), with hyperparameter optimization carried out using Keras Tuner. Model evaluation used time series cross-validation for robustness and a held-out test from 2023 for generalization assessment. The results show that longer windows improve dengue regression accuracy, while classification performance peaked at intermediate windows, suggesting an optimal trade-off between sequence length and generalization. The multitask architecture delivers competitive performance across diseases and tasks, demonstrating the feasibility and advantages of unified modeling strategies for scalable epidemic forecasting in data-limited public health scenarios.