用LSTM联合预测登革热等蚊媒病爆发与病例数
Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data
- 多任务学习框架同时完成爆发检测与病例数预测
- 120天窗口提升登革热预测精度，中等窗口最利于爆发识别
- 适合公共卫生数据少地区做统一疫情预报
本文提出一种基于长短期记忆（LSTM）网络的多任务学习方法，用于联合预测巴西累西腓市登革热、基孔肯雅热和寨卡病毒的疫情爆发与病例数量。利用DataSUS（2017–2023）的历史公共卫生数据，模型同时执行二分类（爆发检测）与回归（病例预测）任务。采用滑动窗口构建时间特征，输入长度设为60、90和120天，通过Keras Tuner进行超参数优化。评估采用时间序列交叉验证以保证稳健性，并使用2023年保留测试集检验泛化能力。结果表明，较长窗口（120天）提升登革热回归准确性，而分类性能在中等窗口达到峰值，表明序列长度与泛化之间存在最优权衡。多任务架构在多种疾病与任务上表现优异，证明统一建模策略在数据有限的公共卫生场景下具有可行性和优势。
原文摘要 · Abstract (English)
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.
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