基于改进SEIR和Logistic增长模型的埃及和阿曼COVID-19预测

Touka M. A. Mahmoud, Mohamed S. A. Abu-Tafesh, Norhan Mohsen ElOcla, A. Mohamed
{"title":"基于改进SEIR和Logistic增长模型的埃及和阿曼COVID-19预测","authors":"Touka M. A. Mahmoud, Mohamed S. A. Abu-Tafesh, Norhan Mohsen ElOcla, A. Mohamed","doi":"10.1109/NILES50944.2020.9257959","DOIUrl":null,"url":null,"abstract":"Understanding the transmission dynamics of the novel coronavirus is the concern that attracted many researchers nowadays. In this paper, two mathematical models, modified SEIR and logistic growth, were implemented in Matlab to predict the transmission of COYID-19 in Egypt and Oman. To estimate the models’ parameters, the reported data were used to fit the models using Nelder-Mead, Levenberg-Marquardt, and Trust-Region-Reflective optimization algorithms. Then, a sensitivity analysis was made to understand the effect of different parameters on the models’ prediction. The application of the two models on the reported data was compared despite their different nature. It was shown and verified that the two models are highly dependent on the parameters’ values, referring to the importance of determining their estimates using an optimization algorithm. It was found out that the most dominant parameter is the one denoting the rate by which susceptible people are protected, which emphasizes the effect of social distancing and quarantine.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Forecasting of COVID-19 in Egypt and Oman using Modified SEIR and Logistic Growth Models\",\"authors\":\"Touka M. A. Mahmoud, Mohamed S. A. Abu-Tafesh, Norhan Mohsen ElOcla, A. Mohamed\",\"doi\":\"10.1109/NILES50944.2020.9257959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Understanding the transmission dynamics of the novel coronavirus is the concern that attracted many researchers nowadays. In this paper, two mathematical models, modified SEIR and logistic growth, were implemented in Matlab to predict the transmission of COYID-19 in Egypt and Oman. To estimate the models’ parameters, the reported data were used to fit the models using Nelder-Mead, Levenberg-Marquardt, and Trust-Region-Reflective optimization algorithms. Then, a sensitivity analysis was made to understand the effect of different parameters on the models’ prediction. The application of the two models on the reported data was compared despite their different nature. It was shown and verified that the two models are highly dependent on the parameters’ values, referring to the importance of determining their estimates using an optimization algorithm. It was found out that the most dominant parameter is the one denoting the rate by which susceptible people are protected, which emphasizes the effect of social distancing and quarantine.\",\"PeriodicalId\":253090,\"journal\":{\"name\":\"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NILES50944.2020.9257959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NILES50944.2020.9257959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

了解新型冠状病毒的传播动力学是当今许多研究人员关注的问题。本文在Matlab中实现了修正SEIR和logistic增长两个数学模型,用于预测2019冠状病毒病在埃及和阿曼的传播。为了估计模型的参数,使用报告的数据使用Nelder-Mead, Levenberg-Marquardt和Trust-Region-Reflective优化算法对模型进行拟合。然后进行敏感性分析,了解不同参数对模型预测的影响。尽管两种模型性质不同,但比较了它们在报告数据上的应用。表明并验证了这两个模型高度依赖于参数值,这是指使用优化算法确定它们的估计的重要性。结果发现,最重要的参数是表示易感人群的保护率的参数,它强调保持社会距离和隔离的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting of COVID-19 in Egypt and Oman using Modified SEIR and Logistic Growth Models
Understanding the transmission dynamics of the novel coronavirus is the concern that attracted many researchers nowadays. In this paper, two mathematical models, modified SEIR and logistic growth, were implemented in Matlab to predict the transmission of COYID-19 in Egypt and Oman. To estimate the models’ parameters, the reported data were used to fit the models using Nelder-Mead, Levenberg-Marquardt, and Trust-Region-Reflective optimization algorithms. Then, a sensitivity analysis was made to understand the effect of different parameters on the models’ prediction. The application of the two models on the reported data was compared despite their different nature. It was shown and verified that the two models are highly dependent on the parameters’ values, referring to the importance of determining their estimates using an optimization algorithm. It was found out that the most dominant parameter is the one denoting the rate by which susceptible people are protected, which emphasizes the effect of social distancing and quarantine.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信