TREA-Net：用于登革热发病率预测的可迁移残差流行病学适配网络
TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting
浏览论文内容
中文总结 AI 辅助
针对新监测系统数据不足的登革热预测问题，提出TREA-Net模型，结合环境时间序列SIR模型与轻量门控残差修正，跨区域迁移知识，在多数据集上提升预测性能，是适用于数据有限机构的预警框架。
中文摘要 AI 辅助
准确的多周登革热预测支持及时的病媒控制干预、疫情准备和医疗资源分配。然而，新建立的监测系统往往缺乏训练可靠神经预测模型所需的历史数据。尽管预训练时间序列模型能提供有前景的零样本预测，但它们的跨域训练可能无法捕捉当地流行病学动态。我们提出TREA-Net，一种用于有限数据下登革热预测的可迁移残差流行病学适配网络。TREA-Net通过环境时间序列易感-感染-恢复（Environmental Time-Series Susceptible-Infected-Recovered）模型的投影增强神经预测骨干，并学习从数据丰富区域到数据稀缺区域可迁移的轻量级门控残差修正。其节点不变设计适配不同位置数量的监测系统，而目标适配仅需学习两个全局参数。我们将哥伦比亚和尼加拉瓜长期登革热监测的知识迁移到墨西哥和马来西亚的8周前预测中，仅使用78或104周的目标数据。在5种神经骨干和10种迁移设置中，TREA-Net在9种设置中优于对应的骨干，且具有统计显著的提升。当与预测基础模型TiRex集成时，它在所有目标数据集中实现最低的平均绝对误差。保形预测进一步保持经验覆盖率，同时将墨西哥的8周预测区间宽度降低29.6%。这些结果证明TREA-Net作为轻量便携预警框架，对监测数据有限的卫生机构具有潜力。
英文摘要
Accurate multi-week dengue forecasting supports timely vector-control interventions, outbreak preparedness, and healthcare resource allocation. However, newly established surveillance systems often lack the historical data needed to train reliable neural forecasting models. Although pretrained time-series models offer promising zero-shot forecasts, their cross-domain training may not capture local epidemiological dynamics. We propose TREA-Net, a Transferable Residual Epidemiological Adaptation Network for dengue forecasting under limited data. TREA-Net augments neural forecasting backbones with projections from an Environmental Time-Series Susceptible-Infected-Recovered model and learns a lightweight gated residual correction transferable from data-rich to data-scarce regions. Its node-invariant design accommodates surveillance systems with different numbers of locations, while target adaptation requires learning only two global parameters. We transfer knowledge from long-running dengue surveillance in Colombia and Nicaragua to 8-week-ahead forecasting in Mexico and Malaysia using only 78 or 104 weeks of target data. Across five neural backbones and ten transfer settings, TREA-Net improves the corresponding backbone in 9 out of 10 settings, with statistically significant gains. When integrated with TiRex, a foundation model for forecasting, it achieves the lowest mean absolute error across all target datasets. Conformal prediction further maintains empirical coverage while reducing 8-week prediction-interval width by 29.6% in Mexico. These results demonstrate TREA-Net's potential as a lightweight and portable early-warning framework for health agencies with limited surveillance data.
