论文标题
改进的流行病学毫无气发的卡尔曼滤波器（混合SEIHCRDV-UKF）模型，用于预测COVID-19。实时数据应用
An improved Epidemiological-Unscented Kalman Filter (Hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data
论文作者
论文摘要
自2019年12月出现在武汉市时，Covid-19的患病率一直是迄今21世纪最严重的健康挑战，每天都在国家卫生系统。然而，大多数旨在描述流行病动力学的数学方法论，都依赖于无法反映其传播的真实本质的确定性模型。在本文中，我们提出了一个SeiHCRDV模型 - 经典爵士隔间模型的扩展/改进，该模型还考虑了在重症监护病房（ICU）（ICU），已故和接种案例的曝光，住院和接纳的人群，并与无用的Kalman Filter（UKF）结合使用，从而提供了动态估计的估算时间依赖性的参数参数。随机方法被认为是必要的，因为观察值和系统方程都以不确定性为特征。显然，这一新考虑对于更有效地检查各种大流行很有用。在长期以来，在265天的时间内检查了该模型的可靠性。从2021年1月开始观察到两次主要感染浪潮，这表明欧洲的疫苗接种开始，基于生产的NRMSE值，提供了令人鼓舞的预测性能。特别重点是证明SeiHCRDV模型的非负性，实现了代表性的基本生殖数R0，并根据估计R0产生的公式证明了疾病平衡的存在和稳定性。该模型的表现不仅优于预测能力的确定性方法，而且还优于采用Kalman过滤器的最新随机模型。
The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV model - an extension/improvement of the classic SIR compartmental model - which also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number R0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimate R0. The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters.
