冠状病毒病(COVID-19)是一种严重的,正在进行,武汉出现的新流行病,中国,2019年12月。截至2021年1月21日,该病毒已感染约1亿人,造成200多万人死亡.本文分析了几种时间序列预测方法,以预测COVID-19在意大利大流行第二波期间(2020年10月13日之后)的传播。自回归移动平均(ARIMA)模型,指数平滑(ETS)的创新状态空间模型,神经网络自回归(NNAR)模型,带Box-Cox变换的三角指数平滑状态空间模型,ARMA错误,以及趋势和季节性成分(TBATS),并采用所有可行的混合组合来预测轻度症状住院的患者数量和重症监护病房(ICU)住院的患者数量。2020年2月21日至2020年10月13日期间的数据摘自意大利卫生部网站(www。salute.gov.it).结果表明:(I)混合模型在捕捉线性、非线性,和季节性大流行模式,显著优于两个时间序列的各自单一模型,(ii)从2020年10月至2020年11月中旬,有轻度症状和ICU患者的COVID-19相关住院人数预计将迅速增加.根据估计,预计必要的普通和重症监护病床将在10天内增加一倍,并在大约20天内增加三倍。这些预测与观察到的趋势一致,证明混合模型可以促进公共卫生当局的决策,尤其是在短期内。
The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic\'s second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health ( www.salute.gov.it ). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities\' decision-making, especially in the short-term.