Multi-chain Fudan-CCDC model for COVID-19-a revisit to Singapore's case.

IF 0.6 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Quantitative Biology Pub Date : 2020-01-01 Epub Date: 2020-11-23 DOI:10.1007/s40484-020-0224-3

Hanshuang Pan, Nian Shao, Yue Yan, Xinyue Luo, Shufen Wang, Ling Ye, Jin Cheng, Wenbin Chen

{"title":"Multi-chain Fudan-CCDC model for COVID-19-a revisit to Singapore's case.","authors":"Hanshuang Pan, Nian Shao, Yue Yan, Xinyue Luo, Shufen Wang, Ling Ye, Jin Cheng, Wenbin Chen","doi":"10.1007/s40484-020-0224-3","DOIUrl":null,"url":null,"abstract":"Background: COVID-19 has been impacting on the whole world critically and constantly since late December 2019. Rapidly increasing infections has raised intense worldwide attention. How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.Methods: We propose the multi-chain Fudan-CCDC model based on the original single-chain model in [Shao et al. 2020] to describe the evolution of COVID-19 in Singapore. Multi-chains can be considered as the superposition of several single chains with different characteristics. We identify the parameters of models by minimizing the penalty function.Results: The numerical simulation results exhibit the multi-chain model performs well on data fitting. Though unsteady the increments are, they could still fall within the range of _30% fluctuation from simulation results.Conclusion: The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak. It can also explain the data from those countries where the single-chain model shows deviation from the data.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 4","pages":"325-335"},"PeriodicalIF":0.6000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0224-3","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s40484-020-0224-3","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/11/23 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}

引用次数: 5

Abstract

Background: COVID-19 has been impacting on the whole world critically and constantly since late December 2019. Rapidly increasing infections has raised intense worldwide attention. How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.

Methods: We propose the multi-chain Fudan-CCDC model based on the original single-chain model in [Shao et al. 2020] to describe the evolution of COVID-19 in Singapore. Multi-chains can be considered as the superposition of several single chains with different characteristics. We identify the parameters of models by minimizing the penalty function.

Results: The numerical simulation results exhibit the multi-chain model performs well on data fitting. Though unsteady the increments are, they could still fall within the range of _30% fluctuation from simulation results.

Conclusion: The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak. It can also explain the data from those countries where the single-chain model shows deviation from the data.

Abstract Image

查看原文本刊更多论文

covid -19多链复旦- ccdc模式——对新加坡案例的重新审视。

背景:自2019年12月下旬以来，2019冠状病毒病(COVID-19)对整个世界产生了严重而持续的影响。迅速增加的感染引起了全世界的高度关注。如何有效、高效地建立新冠肺炎疫情演变模型，对疫情防控具有重要意义。方法:基于[Shao et al. 2020]中原始的单链模型，我们提出了多链Fudan-CCDC模型来描述新加坡COVID-19的演变。多链可以看作是几条具有不同特性的单链的叠加。我们通过最小化惩罚函数来识别模型的参数。结果:数值模拟结果表明，多链模型具有较好的拟合效果。虽然增量是不稳定的，但从模拟结果来看，它们仍然可以在_30%的波动范围内。结论:多链复旦- ccdc模型为早期发现输入性感染者和超级传播者的出现和预测第二次暴发提供了有效途径。它还可以解释那些单链模型显示出与数据偏差的国家的数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Quantitative Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

5.00

自引率

3.20%

发文量

264

期刊介绍： Quantitative Biology is an interdisciplinary journal that focuses on original research that uses quantitative approaches and technologies to analyze and integrate biological systems, construct and model engineered life systems, and gain a deeper understanding of the life sciences. It aims to provide a platform for not only the analysis but also the integration and construction of biological systems. It is a quarterly journal seeking to provide an inter- and multi-disciplinary forum for a broad blend of peer-reviewed academic papers in order to promote rapid communication and exchange between scientists in the East and the West. The content of Quantitative Biology will mainly focus on the two broad and related areas: ·bioinformatics and computational biology, which focuses on dealing with information technologies and computational methodologies that can efficiently and accurately manipulate –omics data and transform molecular information into biological knowledge. ·systems and synthetic biology, which focuses on complex interactions in biological systems and the emergent functional properties, and on the design and construction of new biological functions and systems. Its goal is to reflect the significant advances made in quantitatively investigating and modeling both natural and engineered life systems at the molecular and higher levels. The journal particularly encourages original papers that link novel theory with cutting-edge experiments, especially in the newly emerging and multi-disciplinary areas of research. The journal also welcomes high-quality reviews and perspective articles.