IEEE J Biomed Health Inform. 2022 Apr;26(4):1441-1452. doi: 10.1109/JBHI.2021.3063106. Epub 2022 Apr 14.
This work presents a novel methodology for systematically processing the time series that report the number of positive, recovered and deceased cases from a viral epidemic, such as Covid-19. The main objective is to unveil the evolution of the number of real infected people, and consequently to predict the peak of the epidemic and subsequent evolution. For this purpose, an original nonlinear model relating the raw data with the time-varying geometric ratio of infected people is elaborated, and a Kalman Filter is used to estimate the involved state variables. A hypothetical simulated case is used to show the adequacy and limitations of the proposed method. Then, several countries, including China, South Korea, Italy, Spain, U.K. and the USA, are tested to illustrate its behavior when real-life data are processed. The results obtained clearly show the beneficial effect of the severe lockdowns imposed by many countries worldwide, but also that the softer social distancing measures adopted afterwards have been almost always insufficient to prevent the subsequent virus waves.
本文提出了一种新颖的方法，用于系统地处理报告病毒疫情（如新冠病毒）中阳性、康复和死亡病例数量的时间序列。主要目的是揭示实际感染人数的演变，并预测疫情高峰和后续演变。为此，本文详细阐述了一种将原始数据与随时间变化的感染人数几何比相关联的非线性模型，并使用卡尔曼滤波器来估计所涉及的状态变量。通过一个假设的模拟案例，展示了所提出方法的适用性和局限性。然后，本文测试了包括中国、韩国、意大利、西班牙、英国和美国在内的多个国家，以说明其处理实际数据时的行为。结果清楚地表明，全球许多国家实施的严格封锁措施产生了有益的效果，但随后采取的较为宽松的社交距离措施几乎总是不足以防止后续的病毒波。
