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Abstract：
Initially found in Hubei, Wuhan, and identified as a novel virus of the coronavirus family by the WHO, COVID-19 has spread worldwide at exponential speed, causing millions of deaths and public fear. Currently, the USA, India, Brazil, and other parts of the world are experiencing a secondary wave of COVID-19. However, the medical, mathematical, and pharmaceutical aspects of its transmission, incubation, and recovery processes are still unclear. The classical susceptible-infected-recovered model has limitations in describing the dynamic behavior of COVID-19. Hence, it is necessary to introduce a recursive, latent model to predict the number of future COVID-19 infection cases in the USA. In this article, a dynamic recursive and latent infection model (RLIM) based on the classical SEIR model is proposed to predict the number of COVID-19 infections. Given COVID-19 infection and recovery data for a certain period, the RLIM is able to fit current values and produce an optimal set of parameters with a minimum error rate according to actual reported numbers. With these optimal parameters assigned, the RLIM model then becomes able to produce predictions of infection numbers within a certain period. To locate the turning point of COVID-19 transmission, an initial value for the secondary infection rate is given to the RLIM algorithm for calculation. RLIM will then calculate the secondary infection rates of a continuous time series with an iterative search strategy to speed up the convergence of the prediction outcomes and minimize the maximum square errors. Compared with other forecast algorithms, RLIM is able to adapt the COVID-19 infection curve faster and more accurately and, more importantly, provides a way to identify the turning point in virus transmission by searching for the equilibrium between recoveries and new infections. Simulations of four US states show that with the secondary infection rate ω initially set to 0.5 within the selected latent period of 14 days, RLIM is able to minimize this value at 0.07 and reach an equilibrium condition. A successful forecast is generated using New York state\'s COVID-19 transmission, in which a turning point is predicted to emerge on January 31, 2021.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11071-021-06520-1.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11071-021-06520-1.
摘要：
最初在湖北发现，武汉,并被世界卫生组织鉴定为冠状病毒家族的新型病毒，COVID-19以指数级的速度在世界范围内传播，造成数百万人死亡和公众恐惧。目前,美国,印度,巴西,世界其他地区正在经历COVID-19的二次浪潮。然而,医学,数学,以及其传播的药物方面，孵化,和恢复过程仍不清楚。经典的易感感染-恢复模型在描述COVID-19的动态行为方面存在局限性。因此，有必要引入递归，潜在模型预测美国未来COVID-19感染病例数。在这篇文章中,提出了一种基于经典SEIR模型的动态递归和潜伏感染模型（RLIM）来预测COVID-19感染的数量。给定一段时间的COVID-19感染和恢复数据，RLIM能够拟合当前值，并根据实际报告的数字以最小的错误率生成一组最佳参数。分配了这些最佳参数，然后，RLIM模型能够在一定时期内产生感染数量的预测。为了定位COVID-19传输的转折点，二次感染率的初始值给出给RLIM算法进行计算。然后，RLIM将使用迭代搜索策略计算连续时间序列的继发感染率，以加快预测结果的收敛速度并最小化最大平方误差。与其他预测算法相比，RLIM能够更快、更准确地适应COVID-19感染曲线，更重要的是,提供了一种方法，通过寻找恢复和新感染之间的平衡来确定病毒传播的转折点。对美国四个州的模拟表明，在选定的14天潜伏期内，继发感染率ω最初设定为0.5，RLIM能够在0.07处最小化该值并达到平衡条件。使用纽约州的COVID-19传输生成成功的预测，其中一个转折点预计将在2021年1月31日出现。
■在线版本包含补充材料，可在10.1007/s11071-021-06520-1获得。
■在线版本包含补充材料，可在10.1007/s11071-021-06520-1获得。
