新冠肺炎爆发的数学模型。

IF 2 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2022-01-01 Epub Date: 2021-12-10 DOI:10.1007/s13721-021-00350-2
Arvind Kumar Sinha, Nishant Namdev, Pradeep Shende
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引用次数: 7

摘要

新型冠状病毒SARS-Cov-2是一种大流行性疾病,对健康构成巨大威胁。不同国家的政府及其限制病毒传播的各种禁止措施改变了个人的沟通过程。由于物理和经济因素,人口密度更容易相互作用并传播病毒。我们建立了一个数学模型来呈现新冠肺炎在印度和世界范围内的传播。通过模拟过程,我们发现了新冠肺炎的感染病例、感染致死率和康复率。我们用粗糙集方法对模型进行了验证。在该方法中,我们获得感染病例的准确率为90.19%,新冠肺炎的感染致死率为94%,恢复率为85.57%,与世界卫生组织报告的实际情况大致相同。本文采用广义模拟过程对不同大陆新冠肺炎疫情进行预测。它为新冠肺炎疫情到2021年12月的未来趋势指明了方向,并为了解全球疫情的变化提供了启示。
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Mathematical modeling of the outbreak of COVID-19.

The novel coronavirus SARS-Cov-2 is a pandemic condition and poses a massive menace to health. The governments of different countries and their various prohibitory steps to restrict the virus's expanse have changed individuals' communication processes. Due to physical and financial factors, the population's density is more likely to interact and spread the virus. We establish a mathematical model to present the spread of the COVID-19 in India and worldwide. By the simulation process, we find the infected cases, infected fatality rate, and recovery rate of the COVID-19. We validate the model by the rough set method. In the method, we obtain the accuracy for the infected case is 90.19%, an infection-fatality of COVID-19 is 94%, and the recovery is 85.57%, approximately the same as the actual situation reported WHO. This paper uses the generalized simulation process to predict the outbreak of COVID-19 for different continents. It gives the way of future trends of the COVID-19 outbreak till December 2021 and casts enlightenment about learning the drifts of the outbreak worldwide.

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来源期刊
CiteScore
5.40
自引率
4.30%
发文量
43
期刊介绍: NetMAHIB publishes original research articles and reviews reporting how graph theory, statistics, linear algebra and machine learning techniques can be effectively used for modelling and analysis in health informatics and bioinformatics. It aims at creating a synergy between these disciplines by providing a forum for disseminating the latest developments and research findings; hence, results can be shared with readers across institutions, governments, researchers, students, and the industry. The journal emphasizes fundamental contributions on new methodologies, discoveries and techniques that have general applicability and which form the basis for network based modelling, knowledge discovery, knowledge sharing and decision support to the benefit of patients, healthcare professionals and society in traditional and advanced emerging settings, including eHealth and mHealth .
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