Modified exponential time series model with prediction of total COVID-19 cases in Belgium, Czech Republic, Poland and Switzerland

Q4 Mathematics Statistics in Transition Pub Date : 2022-09-01 DOI:10.2478/stattrans-2022-0035

W. Permpoonsinsup, R. Sunthornwat

{"title":"Modified exponential time series model with prediction of total COVID-19 cases in Belgium, Czech Republic, Poland and Switzerland","authors":"W. Permpoonsinsup, R. Sunthornwat","doi":"10.2478/stattrans-2022-0035","DOIUrl":null,"url":null,"abstract":"Abstract The coronavirus (COVID-19) pandemic affected every country worldwide. In particular, outbreaks in Belgium, the Czech Republic, Poland and Switzerland entered the second wave and was exponentially increasing between July and November, 2020. The aims of the study are: to estimate the compound growth rate, to develop a modified exponential time-series model compared with the hyperbolic time-series model, and to estimate the optimal parameters for the models based on the exponential least-squares, three selected points, partial-sums methods, and the hyperbolic least-squares for the daily COVID-19 cases in Belgium, the Czech Republic, Poland and Switzerland. The speed and spreading power of COVID-19 infections were obtained by using derivative and root-mean-squared methods, respectively. The results show that the exponential least-squares method was the most suitable for the parameter estimation. The compound growth rate of COVID-19 infection was the highest in Switzerland, and the speed and spreading power of COVID-19 infection were the highest in Poland between July and November, 2020.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Transition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/stattrans-2022-0035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Mathematics","Score":null,"Total":0}

引用次数: 0

Abstract

Abstract The coronavirus (COVID-19) pandemic affected every country worldwide. In particular, outbreaks in Belgium, the Czech Republic, Poland and Switzerland entered the second wave and was exponentially increasing between July and November, 2020. The aims of the study are: to estimate the compound growth rate, to develop a modified exponential time-series model compared with the hyperbolic time-series model, and to estimate the optimal parameters for the models based on the exponential least-squares, three selected points, partial-sums methods, and the hyperbolic least-squares for the daily COVID-19 cases in Belgium, the Czech Republic, Poland and Switzerland. The speed and spreading power of COVID-19 infections were obtained by using derivative and root-mean-squared methods, respectively. The results show that the exponential least-squares method was the most suitable for the parameter estimation. The compound growth rate of COVID-19 infection was the highest in Switzerland, and the speed and spreading power of COVID-19 infection were the highest in Poland between July and November, 2020.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于修正指数时间序列模型的比利时、捷克、波兰和瑞士COVID-19总病例预测

摘要冠状病毒（新冠肺炎）大流行影响了世界各地的每个国家。特别是，比利时、捷克共和国、波兰和瑞士的疫情进入第二波，并在2020年7月至11月呈指数级增长。该研究的目的是：估计复合增长率，开发一个与双曲线时间序列模型相比较的修正指数时间序列模型，并基于指数最小二乘法、三个选点法、偏模法和双曲线最小二乘法估计比利时、捷克共和国、，波兰和瑞士。分别采用导数法和均方根法获得新冠肺炎感染的速度和传播力。结果表明，指数最小二乘法最适合于参数估计。2020年7月至11月，新冠肺炎感染的复合增长率在瑞士最高，新冠肺炎感染的速度和传播力在波兰最高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Statistics in Transition Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

9 weeks

期刊介绍： Statistics in Transition (SiT) is an international journal published jointly by the Polish Statistical Association (PTS) and the Central Statistical Office of Poland (CSO/GUS), which sponsors this publication. Launched in 1993, it was issued twice a year until 2006; since then it appears - under a slightly changed title, Statistics in Transition new series - three times a year; and after 2013 as a regular quarterly journal." The journal provides a forum for exchange of ideas and experience amongst members of international community of statisticians, data producers and users, including researchers, teachers, policy makers and the general public. Its initially dominating focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy has gradually been extended to embracing statistical problems related to development and modernization of the system of public (official) statistics, in general.