冠状病毒大流行官方统计数据监测、分析和预测的新机遇模型

IF 3.7 4区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Romanian Journal of Information Science and Technology Pub Date : 2023-03-24 DOI:10.59277/romjist.2023.1.04
S. Abramov, S. Travin, G. Duca
{"title":"冠状病毒大流行官方统计数据监测、分析和预测的新机遇模型","authors":"S. Abramov, S. Travin, G. Duca","doi":"10.59277/romjist.2023.1.04","DOIUrl":null,"url":null,"abstract":"At the beginning of 2020, it became obvious that the coronavirus disease 2019 (COVID-19) pandemic will have a fairly significant scale and duration. There was an unmet need for the analysis and forecast of the development of events. The forecast was needed to make the managerial decisions in terms of knowledge on the dynamics of the pandemic, considering and analyzing the incoming official statistics about the pandemic, modeling and predicting the behavior of this statistics. Due to the objective and subjective factors, the available statistics is far from the unknown true data regarding the pandemic. Therefore, strictly speaking, it was necessary to model and predict not the dynamics of the pandemic, but the dynamics of the official (i.e. government) statistics on the pandemic. This paper proposes a new model, referred to as the new opportunities model, to monitor, analyze and forecast the government statistics on COVID-19 pandemic. A modeling approach is offered in this regard. The modeling approach is important as it answers simple questions on what awaits us in the near future, which is the current phase of the pandemic and when all this will be over. The new opportunities model is applied to three different countries in terms of area, economy and population, namely Russia, Romania and Moldova, plus the Campania region in Italy, and proves to be efficient over other similar models including the classical Susceptible-Infected (SI) model.","PeriodicalId":54448,"journal":{"name":"Romanian Journal of Information Science and Technology","volume":"1 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"New Opportunities Model for Monitoring, Analyzing and Forecasting the Official Statistics on Coronavirus Disease Pandemic\",\"authors\":\"S. Abramov, S. Travin, G. Duca\",\"doi\":\"10.59277/romjist.2023.1.04\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At the beginning of 2020, it became obvious that the coronavirus disease 2019 (COVID-19) pandemic will have a fairly significant scale and duration. There was an unmet need for the analysis and forecast of the development of events. The forecast was needed to make the managerial decisions in terms of knowledge on the dynamics of the pandemic, considering and analyzing the incoming official statistics about the pandemic, modeling and predicting the behavior of this statistics. Due to the objective and subjective factors, the available statistics is far from the unknown true data regarding the pandemic. Therefore, strictly speaking, it was necessary to model and predict not the dynamics of the pandemic, but the dynamics of the official (i.e. government) statistics on the pandemic. This paper proposes a new model, referred to as the new opportunities model, to monitor, analyze and forecast the government statistics on COVID-19 pandemic. A modeling approach is offered in this regard. The modeling approach is important as it answers simple questions on what awaits us in the near future, which is the current phase of the pandemic and when all this will be over. The new opportunities model is applied to three different countries in terms of area, economy and population, namely Russia, Romania and Moldova, plus the Campania region in Italy, and proves to be efficient over other similar models including the classical Susceptible-Infected (SI) model.\",\"PeriodicalId\":54448,\"journal\":{\"name\":\"Romanian Journal of Information Science and Technology\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Romanian Journal of Information Science and Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.59277/romjist.2023.1.04\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Romanian Journal of Information Science and Technology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.59277/romjist.2023.1.04","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 1

摘要

2020年初,很明显,2019冠状病毒病(新冠肺炎)大流行将具有相当大的规模和持续时间。对事件发展的分析和预测的需求没有得到满足。需要预测来根据对疫情动态的了解做出管理决策,考虑和分析即将到来的关于疫情的官方统计数据,对这些统计数据的行为进行建模和预测。由于客观和主观因素,现有的统计数据与未知的疫情真实数据相去甚远。因此,严格来说,有必要建模和预测的不是疫情的动态,而是官方(即政府)疫情统计数据的动态。本文提出了一种新的模型,称为新机会模型,用于监测、分析和预测新冠肺炎疫情的政府统计数据。在这方面提供了一种建模方法。建模方法很重要,因为它回答了在不久的将来等待我们的简单问题,即疫情的当前阶段,以及这一切何时结束。新的机会模型在面积、经济和人口方面适用于三个不同的国家,即俄罗斯、罗马尼亚和摩尔多瓦,以及意大利的坎帕尼亚地区,并被证明比其他类似模型有效,包括经典的易感感染(SI)模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
New Opportunities Model for Monitoring, Analyzing and Forecasting the Official Statistics on Coronavirus Disease Pandemic
At the beginning of 2020, it became obvious that the coronavirus disease 2019 (COVID-19) pandemic will have a fairly significant scale and duration. There was an unmet need for the analysis and forecast of the development of events. The forecast was needed to make the managerial decisions in terms of knowledge on the dynamics of the pandemic, considering and analyzing the incoming official statistics about the pandemic, modeling and predicting the behavior of this statistics. Due to the objective and subjective factors, the available statistics is far from the unknown true data regarding the pandemic. Therefore, strictly speaking, it was necessary to model and predict not the dynamics of the pandemic, but the dynamics of the official (i.e. government) statistics on the pandemic. This paper proposes a new model, referred to as the new opportunities model, to monitor, analyze and forecast the government statistics on COVID-19 pandemic. A modeling approach is offered in this regard. The modeling approach is important as it answers simple questions on what awaits us in the near future, which is the current phase of the pandemic and when all this will be over. The new opportunities model is applied to three different countries in terms of area, economy and population, namely Russia, Romania and Moldova, plus the Campania region in Italy, and proves to be efficient over other similar models including the classical Susceptible-Infected (SI) model.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Romanian Journal of Information Science and Technology
Romanian Journal of Information Science and Technology 工程技术-计算机:理论方法
CiteScore
5.50
自引率
8.60%
发文量
0
审稿时长
>12 weeks
期刊介绍: The primary objective of this journal is the publication of original results of research in information science and technology. There is no restriction on the addressed topics, the only acceptance criterion being the originality and quality of the articles, proved by independent reviewers. Contributions to recently emerging areas are encouraged. Romanian Journal of Information Science and Technology (a publication of the Romanian Academy) is indexed and abstracted in the following Thomson Reuters products and information services: • Science Citation Index Expanded (also known as SciSearch®), • Journal Citation Reports/Science Edition.
期刊最新文献
XOR-Based Detector of Different Decisions on Anomalies in the Computer Network Traffic Twitter's Mirroring of the 2022 Energy Crisis: What It Teaches Decision-Makers - A Preliminary Study Binary Anarchic Society Optimization for Feature Selection Speech Emotion Recognition Using Deep Neural Networks, Transfer Learning, and Ensemble Classification Techniques Using Swear Words Increases the Irritability – a Study Using AI Algorithms
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1