Analyzing the trend in COVID-19 data: The structural break approach

Nityananda Sarkar, Kushal BANİK CHOWDHURY
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引用次数: 0

Abstract

In this paper, we have considered three important variables concerning COVID-19 viz., (i) the number of daily new cases, (ii) the number of daily total cases, and (iii) the number of daily deaths, and proposed a modelling procedure, so that the nature of trend in these series could be studied appropriately and then used for identifying the current phase of the pandemic including the phase of containment, if happening /happened, in any country. The proposed modelling procedure gives due consideration to structural breaks in the series. The data from four countries, Brazil, India, Italy and the UK, have been used to study the efficacy of the proposed model. Regarding the phase of infection in these countries, we have found, using data till 19 May 2020, that both Brazil and India are in the increasing phase with infections rising up and further up, but Italy and the UK are in decreasing/containing phase suggesting that these two countries are expected to be free of this pandemic in due course of time provided their respective trend continues. The forecast performance of this model has also established its superiority, as compared to two other standard trend models viz., polynomial and exponential trend models.
COVID-19数据趋势分析:结构断裂法
在本文中,我们考虑了与新冠肺炎有关的三个重要变量,即(i)每日新增病例数、(ii)每日总病例数和(iii)每日死亡人数,并提出了建模程序,以便对这些系列中趋势的性质进行适当的研究,然后用于确定大流行的当前阶段,包括在任何国家发生/发生的遏制阶段。拟议的建模程序充分考虑了序列中的结构断裂。来自巴西、印度、意大利和英国四个国家的数据已被用于研究拟议模型的功效。关于这些国家的感染阶段,我们使用截至2020年5月19日的数据发现,巴西和印度都处于感染增加阶段,感染率不断上升,但意大利和英国处于减少/控制阶段,这表明如果这两个国家各自的趋势继续下去,预计它们将在适当的时候摆脱这一流行病。与其他两种标准趋势模型,即多项式和指数趋势模型相比,该模型的预测性能也确立了其优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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15 weeks
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