Forecasting Seasonal Peaks of Pediatric Respiratory Infections Using an Alert-Based Model Combining SIR Dynamics and Historical Trends in Santiago, Chile

TL;DR

This paper introduces an alert-based forecasting model that combines SIR dynamics and historical data to predict pediatric respiratory infection peaks in Santiago, Chile, enabling better hospital planning and preparedness.

Contribution

The study develops a novel integrated model that accurately predicts the timing and magnitude of infection peaks using real-time alerts and historical trends.

Findings

Peak date can be predicted about one month in advance.

Peak magnitude is reliably estimated around ten days before peak.

Model performs well in retrospective and real-world settings during 2023-2024.

Abstract

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.