Suppr超能文献

采用盖代可靠性方法对新加坡新冠疫情数据进行交叉验证。

Singapore COVID-19 data cross-validation by the Gaidai reliability method.

作者信息

Gaidai Oleg, Yakimov Vladimir, Sun Jiayao, van Loon Eric-Jan

机构信息

Shanghai Ocean University, Shanghai, China.

Jiangsu University of Science and Technology, Zhenjiang, China.

出版信息

Npj Viruses. 2023 Dec 13;1(1):9. doi: 10.1038/s44298-023-00006-0.

Abstract

Novel coronavirus infection (COVID-19) has exserted certain burden on global public health, spreading around the world with reportedly low mortality and morbidity. This study advocates novel bio and health system reliability approach, especially suitable for multi-regional environmental and health systems. Advocated spatiotemporal method has been cross-validated, versus well established bivariate Weibull method, based on available raw clinical dataset. The purpose of this study was to assess risks of excessive coronavirus death rates, that may occur within any given time horizon, and in any region or district of interest. This study aims at benchmarking of the novel Gaidai bio-reliability method, allowing accurate assessment of national public health system risks, for the years to come. Novel bio-system reliability approach is particularly suitable for multi-regional environmental and health systems, monitored for a sufficiently representative period of time. In case when underlying bio-system is stationary, or the underlying trend is known, long-term future death rate risk assessment can be done, and confidence intervals can be generated. Advocated methodology may to be useful for a wide variety of public health applications, thus, it is not limited to the example, considered here.

摘要

新型冠状病毒感染(COVID-19)给全球公共卫生带来了一定负担,在全球传播,据报道其死亡率和发病率较低。本研究提倡一种新型的生物与健康系统可靠性方法,特别适用于多区域环境与健康系统。基于可用的原始临床数据集,将提倡的时空方法与成熟的双变量威布尔方法进行了交叉验证。本研究的目的是评估在任何给定时间范围内以及任何感兴趣的区域或地区可能出现的冠状病毒死亡率过高的风险。本研究旨在对新型盖代生物可靠性方法进行基准测试,以便在未来几年准确评估国家公共卫生系统风险。新型生物系统可靠性方法特别适用于在足够有代表性的时间段内进行监测的多区域环境与健康系统。如果基础生物系统是稳定的,或者基础趋势是已知的,就可以进行长期未来死亡率风险评估,并生成置信区间。提倡的方法可能对广泛的公共卫生应用有用,因此,它不限于这里所考虑的示例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a26/11721124/e84baa8c41ae/44298_2023_6_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验