New Opportunities Model for Monitoring, Analyzing and Forecasting the Official Statistics on Coronavirus Disease Pandemic

IF 3.7 4区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
S. Abramov, S. Travin, G. Duca
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引用次数: 1

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.
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
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。
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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.
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