COVID-19 pandemic course 2020-2022: description by methods of mathematical statistics and discrete mathematical analysis

IF 0.7 Q4 GEOSCIENCES, MULTIDISCIPLINARY
Alexei Gvishiani, A. Odintsova, E. Rovenskaya, Grigory Boyarshinov, I. Belov, M. Dobrovolsky
{"title":"COVID-19 pandemic course 2020-2022: description by methods of mathematical statistics and discrete mathematical analysis","authors":"Alexei Gvishiani, A. Odintsova, E. Rovenskaya, Grigory Boyarshinov, I. Belov, M. Dobrovolsky","doi":"10.2205/2023es000839","DOIUrl":null,"url":null,"abstract":"The paper describes the course of the COVID-19 pandemic using a combination of mathematical statistics and discrete mathematical analysis (DMA) methods. The method of regression derivatives and FCARS algorithm as components of DMA will be for the first time tested outside of geophysics problems. The algorithm is applied to time series of the number of new cases of COVID-19 infections per day for some regions of Russia and the Republic of Austria. This allowed to assess the nature and anomalies of pandemic spread as well as restrictive measures and decisions taken in terms of the administration of countries and territories. It was shown that these methods can be used to identify time intervals of change in the nature of the incidence rate and areas with the most severe course of the epidemic. This made it possible to identify the most significant restrictive measures that allowed to reduce the growth of the disease.","PeriodicalId":44680,"journal":{"name":"Russian Journal of Earth Sciences","volume":"4 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Earth Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2205/2023es000839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

The paper describes the course of the COVID-19 pandemic using a combination of mathematical statistics and discrete mathematical analysis (DMA) methods. The method of regression derivatives and FCARS algorithm as components of DMA will be for the first time tested outside of geophysics problems. The algorithm is applied to time series of the number of new cases of COVID-19 infections per day for some regions of Russia and the Republic of Austria. This allowed to assess the nature and anomalies of pandemic spread as well as restrictive measures and decisions taken in terms of the administration of countries and territories. It was shown that these methods can be used to identify time intervals of change in the nature of the incidence rate and areas with the most severe course of the epidemic. This made it possible to identify the most significant restrictive measures that allowed to reduce the growth of the disease.
2019冠状病毒病大流行课程2020-2022:用数理统计和离散数学分析方法进行描述
本文采用数理统计和离散数学分析(DMA)相结合的方法描述了COVID-19大流行的过程。回归导数方法和FCARS算法作为DMA的组成部分将首次在地球物理问题之外进行测试。该算法应用于俄罗斯和奥地利共和国一些地区每天新发COVID-19感染病例数的时间序列。这使我们能够评估大流行病传播的性质和异常情况,以及在国家和领土管理方面采取的限制性措施和决定。结果表明,这些方法可用于确定发病率性质变化的时间间隔和流行病病程最严重的地区。这就有可能确定能够减少疾病发展的最重要的限制性措施。
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 求助全文
来源期刊
Russian Journal of Earth Sciences
Russian Journal of Earth Sciences GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
1.90
自引率
15.40%
发文量
41
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信