Purely data-driven exploration of COVID-19 pandemic after three months of the outbreak

IF 0.5 Q4 MULTIDISCIPLINARY SCIENCES
Saydaliev Hayot Berk, S. Kadyrov
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引用次数: 2

Abstract

It has been three months since the novel coronavirus (COVID-19) pandemic outbreak. Many research studies were carried to understand its epidemiological characteristics in the early phase of the disease outbreak. The current study is yet another contribution to better understand the disease properties by parameter estimation of mathematical SIR epidemic modeling. The authors use Johns Hopkins University dataset to estimate the basic reproduction number of COVID-19 for representative countries (Japan, Germany, Italy, France, and Netherlands) selected using cluster analysis. As a by-product, the authors estimate transmission, recovery, and death rates for each selected country and carry statistical tests to see if there are any significant differences.
在疫情爆发三个月后,对COVID-19大流行进行纯数据驱动的探索
新型冠状病毒(COVID-19)大流行已经过去3个月了。为了解该病暴发初期的流行病学特征,开展了许多研究。目前的研究是通过数学SIR流行病模型的参数估计更好地了解疾病特性的又一贡献。作者使用约翰霍普金斯大学的数据集,通过聚类分析估计了代表性国家(日本、德国、意大利、法国和荷兰)的COVID-19基本再现数。作为副产品,作者估计了每个选定国家的传播、恢复和死亡率,并进行了统计测试,以查看是否存在任何显著差异。
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来源期刊
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
24 weeks
期刊介绍: Journal of Mathematical and Fundamental Sciences welcomes full research articles in the area of Mathematics and Natural Sciences from the following subject areas: Astronomy, Chemistry, Earth Sciences (Geodesy, Geology, Geophysics, Oceanography, Meteorology), Life Sciences (Agriculture, Biochemistry, Biology, Health Sciences, Medical Sciences, Pharmacy), Mathematics, Physics, and Statistics. New submissions of mathematics articles starting in January 2020 are required to focus on applied mathematics with real relevance to the field of natural sciences. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.
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