Acute respiratory infections (ARI) are a major cause of pediatric hospitalization in Chile, producing marked winter increases in demand that challenge hospital planning. This study presents an alert-based forecasting model to predict the timing and magnitude of ARI hospitalization peaks in Santiago. The approach integrates a seasonal SIR model with a historical mobile predictor, activated by a derivative-based alert system that detects early epidemic growth. Daily hospitalization data from DEIS were smoothed using a 15-day moving average and Savitzky-Golay filtering, and parameters were estimated using a penalized loss function to reduce sensitivity to noise. Retrospective evaluation and real-world implementation in major Santiago pediatric hospitals during 2023 and 2024 show that peak date can be anticipated about one month before the event and predicted with high accuracy two weeks in advance. Peak magnitude becomes informative roughly ten days before the peak and stabilizes one week prior. The model provides a practical and interpretable tool for hospital preparedness.

翻译：急性呼吸道感染是智利儿科住院的主要原因，其导致的冬季需求激增对医院规划构成挑战。本研究提出一种基于警报的预测模型，用于预测圣地亚哥地区急性呼吸道感染住院高峰的时序与规模。该方法将季节性SIR模型与历史移动预测因子相结合，通过基于导数的警报系统（检测早期疫情增长态势）激活。研究采用15日移动平均与Savitzky-Golay滤波对DEIS的每日住院数据进行平滑处理，并通过惩罚损失函数进行参数估计以降低噪声敏感性。2023-2024年在圣地亚哥主要儿科医院的回顾性评估与实际应用表明：高峰日期可提前约一个月预警，并在两周前实现高精度预测；高峰规模约在峰值前十日具备参考价值，并于一周前趋于稳定。该模型为医院应急准备提供了实用且可解释的决策工具。