Dynamic demand forecasting and capacity assessment for emergency care system: a hybrid time series and machine learning approach with evidence from Beijing

Accurately forecasting the demand of emergency care system (ECS) and assessing the system capacity are critical for effective resource allocation and crisis preparedness. This study aims to develop a robust hybrid forecasting framework to predict patient demand and identify critical capacity thresholds in Beijing’s ECS under varying operational conditions. Using comprehensive data on 120 ambulance dispatches and hospital emergency department admissions from 2019 to 2024, the data are explicitly categorized into normal and peak periods, enabling state-specific analyses that reflect the distinct operational dynamics of each mode. The analysis reveals that normal period data exhibit strong seasonality and nonlinearity, while peak periods follow a three-phase pattern: initial resource stress, capacity expansion, and system recovery. To capture the complex temporal dynamics of emergency care system, both time series models (ARIMA, VAR, Prophet) and machine learning models (XGBoost, LightGBM, CatBoost) were applied, the comparison analysis results show that machine learning models, particularly CatBoost and LightGBM, outperform traditional approaches in capturing nonlinear relationships, while Prophet excels in long-term trend and seasonality modeling. Consequently, an ensemble average model combining these three models could yield robust and accurate forecasts. Further scenario tests based on the ensemble model identified two key system capacity expansion thresholds: emergency bed use nearing 2,100 beds and a bed usage-to-patient ratio dropping to 6.5%–6.0%. The framework offers a practical, transferable approach for forecasting demand and guiding resource allocation in urban emergency systems.