Department of Environmental and Preventive Sciences, University of Ferrara, Ferrara, Italy.
Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, Padova, 35131, Italy.
J Med Syst. 2023 Aug 5;47(1):84. doi: 10.1007/s10916-023-01982-9.
The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.
COVID-19 大流行的经验表明，及时监测 ICU 入院情况非常重要。能够及时预测疫情对 ICU 床位占用的影响，是充分管理医疗保健系统的关键问题。尽管如此，意大利关于预测 COVID-19 模型的大多数文献都侧重于预测感染人数，而将普通住院和 ICU 入住率的趋势置于次要地位。本研究旨在提出一种基于 ETS 模型的 ICU 入院 ETS 方法（指数平滑时间序列）时间序列预测工具。针对受疫情影响最严重的地区，如威尼托、伦巴第、艾米利亚-罗马涅和皮埃蒙特，展示了预测模型的结果。对于所有考虑的地理区域，观察到的和预测的 ICU 入院人数之间的平均绝对百分比误差 (MAPE) 仍低于 11%。在这种流行病学背景下，所提出的 ETS 预测模型可用于及时监测 COVID-19 疾病对医疗保健系统的影响，不仅在大流行的早期阶段，而且在疫苗接种运动期间，以便快速采取可能的预防干预措施。
