基于统计传播模型的新冠肺炎疫情早期分析

IF 0.8 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Statistics in Biosciences Pub Date : 2021-01-01 Epub Date: 2020-04-02 DOI:10.1007/s12561-020-09277-0
Yifan Zhu, Ying Qing Chen
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引用次数: 52

摘要

自2019年12月以来,一种新型冠状病毒(COVID-19)引起的疾病感染了许多人,截至2020年3月17日,累计确诊病例已接近18万例。据信,新冠肺炎疫情是在中国湖北省人口超过1100万的大都市武汉的一个海鲜市场爆发的。我们引入了统计疾病传播模型,利用病例症状发作数据估计中国早期疫情的传播力,并对COVID-19疾病自然史的各种假设进行了敏感性分析。我们将传播模型拟合到2020年2月11日之前的几个公开来源的疫情数据,并估计了武汉市的封锁干预效果。根据潜伏期和传染期内相对传染性的合理分布假设,估计r0在2.7至4.2之间。95%置信区间r0也有报道。讨论了诸如数据质量问题和不同建模方法的比较等潜在问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak.

On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak.

On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak.

On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak.

Since December 2019, a disease caused by a novel strain of coronavirus (COVID-19) had infected many people and the cumulative confirmed cases have reached almost 180,000 as of 17, March 2020. The COVID-19 outbreak was believed to have emerged from a seafood market in Wuhan, a metropolis city of more than 11 million population in Hubei province, China. We introduced a statistical disease transmission model using case symptom onset data to estimate the transmissibility of the early-phase outbreak in China, and provided sensitivity analyses with various assumptions of disease natural history of the COVID-19. We fitted the transmission model to several publicly available sources of the outbreak data until 11, February 2020, and estimated lock down intervention efficacy of Wuhan city. The estimated R 0 was between 2.7 and 4.2 from plausible distribution assumptions of the incubation period and relative infectivity over the infectious period. 95% confidence interval of R 0 were also reported. Potential issues such as data quality concerns and comparison of different modelling approaches were discussed.

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来源期刊
Statistics in Biosciences
Statistics in Biosciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.00
自引率
0.00%
发文量
28
期刊介绍: Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science. SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.
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