Precise outbreak forecasting of infectious diseases is essential for effective public health responses and epidemic control. The increased availability of machine learning (ML) methods for time-series forecasting presents an enticing avenue to enhance outbreak forecasting. Though the COVID-19 outbreak demonstrated the value of applying ML models to predict epidemic profiles, using ML models to forecast endemic diseases remains underexplored. In this work, we present ForecastNet-XCL (an ensemble model based on XGBoost+CNN+BiLSTM), a deep learning hybrid framework designed to addresses this gap by creating accurate multi-week RSV forecasts up to 100 weeks in advance based on climate and temporal data, without access to real-time surveillance on RSV. The framework combines high-resolution feature learning with long-range temporal dependency capturing mechanisms, bolstered by an autoregressive module trained on climate-controlled lagged relations. Stochastic inference returns probabilistic intervals to inform decision-making. Evaluated across 34 U.S. states, ForecastNet-XCL reliably outperformed statistical baselines, individual neural nets, and conventional ensemble methods in both within- and cross-state scenarios, sustaining accuracy over extended forecast horizons. Training on climatologically diverse datasets enhanced generalization furthermore, particularly in locations having irregular or biennial RSV patterns. ForecastNet-XCL's efficiency, performance, and uncertainty-aware design make it a deployable early-warning tool amid escalating climate pressures and constrained surveillance resources.

翻译：传染病爆发的精准预测对于有效的公共卫生响应和疫情控制至关重要。时间序列预测机器学习方法的日益普及为增强疫情预测提供了一条诱人的途径。尽管COVID-19疫情证明了应用机器学习模型预测流行曲线的价值，但利用机器学习模型预测地方性疾病的研究仍显不足。本研究提出ForecastNet-XCL（一种基于XGBoost+CNN+BiLSTM的集成模型），该深度学习混合框架旨在通过基于气候与时间数据（无需获取呼吸道合胞病毒的实时监测数据）创建提前多达100周的精准多周RSV预测，以填补这一空白。该框架将高分辨率特征学习与长程时间依赖性捕捉机制相结合，并通过在气候控制滞后关系上训练的自回归模块进行增强。随机推理可返回概率区间以支持决策制定。在美国34个州范围内的评估表明，无论在州内还是跨州场景下，ForecastNet-XCL均持续优于统计基线模型、独立神经网络及传统集成方法，并在延长预测时域内保持准确性。在气候多样性数据集上的训练进一步增强了模型的泛化能力，尤其在具有不规则或两年期RSV模式的地区表现突出。ForecastNet-XCL的高效性、优异性能及不确定性感知设计，使其在气候压力加剧与监测资源受限的背景下，成为一种可部署的早期预警工具。