基于时间相关自举数据的covid - 19元启发式优化预测方法

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2020-04-07 DOI:10.1101/2020.04.02.20050153

L. Fenga, Carlo Del Castello

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引用次数: 7

摘要

提出了一种利用运筹学算法的搜索能力和自举技术的能力的复合方法。由此产生的算法已经成功测试，可以预测CoViD19病毒在意大利的流行曲线所达到的转折点。最后将给出未来的研究路线，包括将该方法推广到一组广泛的分布。

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CoViD19 Meta heuristic optimization based forecast method on time dependent bootstrapped data

A compounded method, exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques, is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the CoViD19 virus in Italy. Futures lines of research, which include the generalization of the method to a broad set of distribution, will be finally given.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.