Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data
This work addresses scalable epidemic forecasting for public health officials in data-limited settings, though it appears incremental as it applies existing multitask LSTM methods to a specific domain.
This paper tackled the problem of predicting arboviral outbreaks and case counts for dengue, chikungunya, and Zika in Recife, Brazil, using a multitask LSTM approach on public health data from 2017-2023, finding that longer input windows improved dengue regression accuracy while intermediate windows optimized classification performance.
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.