• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

考虑时变传播率的延迟多尺度传染病模型中流感防控的疫苗和抗病毒药物的最优分配

Optimal Allocation of Vaccine and Antiviral Drugs for Influenza Containment over Delayed Multiscale Epidemic Model considering Time-Dependent Transmission Rate.

机构信息

Department of Electrical and Computer Engineering, University of Kashan, Iran.

Electrical and Electronic Engineering Department, Shahed University, Tehran, Iran.

出版信息

Comput Math Methods Med. 2021 Oct 18;2021:4348910. doi: 10.1155/2021/4348910. eCollection 2021.

DOI:10.1155/2021/4348910
PMID:34707682
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8545521/
Abstract

In this study, two types of epidemiological models called "within host" and "between hosts" have been studied. The within-host model represents the innate immune response, and the between-hosts model signifies the SEIR (susceptible, exposed, infected, and recovered) epidemic model. The major contribution of this paper is to break the chain of infectious disease transmission by reducing the number of susceptible and infected people via transferring them to the recovered people group with vaccination and antiviral treatment, respectively. Both transfers are considered with time delay. In the first step, optimal control theory is applied to calculate the optimal final time to control the disease within a host's body with a cost function. To this end, the vaccination that represents the effort that converts healthy cells into resistant-to-infection cells in the susceptible individual's body is used as the first control input to vaccinate the susceptible individual against the disease. Moreover, the next control input (antiviral treatment) is applied to eradicate the concentrations of the virus and convert healthy cells into resistant-to-infection cells simultaneously in the infected person's body to treat the infected individual. The calculated optimal time in the first step is considered as the delay of vaccination and antiviral treatment in the SEIR dynamic model. Using Pontryagin's maximum principle in the second step, an optimal control strategy is also applied to an SEIR mathematical model with a nonlinear transmission rate and time delay, which is computed as optimal time in the first step. Numerical results are consistent with the analytical ones and corroborate our theoretical results.

摘要

在这项研究中,研究了两种称为“宿主内”和“宿主间”的流行病学模型。宿主内模型代表先天免疫反应,而宿主间模型表示 SEIR(易感、暴露、感染和恢复)传染病模型。本文的主要贡献是通过接种疫苗和抗病毒治疗分别将易感和感染人群转移到恢复人群,从而打破传染病传播链。这两种转移都考虑了时滞。在第一步中,应用最优控制理论计算以成本函数控制宿主体内疾病的最优最终时间。为此,使用疫苗接种作为第一种控制输入,将健康细胞转化为易感个体体内抗感染细胞,以对易感个体进行疾病接种。此外,下一个控制输入(抗病毒治疗)同时应用于受感染个体体内消除病毒浓度并将健康细胞转化为抗感染细胞,以治疗受感染个体。第一步中计算出的最优时间被视为 SEIR 动态模型中疫苗接种和抗病毒治疗的延迟。在第二步中使用庞特里亚金极大值原理,还应用最优控制策略到具有非线性传输率和时滞的 SEIR 数学模型中,该模型作为第一步中的最优时间进行计算。数值结果与分析结果一致,证实了我们的理论结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/4bdac8cf1f17/CMMM2021-4348910.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/e754f9360918/CMMM2021-4348910.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/eef00763dc1e/CMMM2021-4348910.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/d672b79cc708/CMMM2021-4348910.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/d3b27439356b/CMMM2021-4348910.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/06d47561b4eb/CMMM2021-4348910.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/1bcb9bf9aec8/CMMM2021-4348910.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/bd5591d2f409/CMMM2021-4348910.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/d19f926cdc8b/CMMM2021-4348910.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/863398028117/CMMM2021-4348910.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/2d696ace0ed3/CMMM2021-4348910.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/186770ef7811/CMMM2021-4348910.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/4ee330cb3213/CMMM2021-4348910.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/b63b178718df/CMMM2021-4348910.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/82b1028703dd/CMMM2021-4348910.014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/f2cd247050e2/CMMM2021-4348910.015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/b0f93eaea828/CMMM2021-4348910.016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/c377458e21ba/CMMM2021-4348910.017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/4bdac8cf1f17/CMMM2021-4348910.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/e754f9360918/CMMM2021-4348910.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/eef00763dc1e/CMMM2021-4348910.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/d672b79cc708/CMMM2021-4348910.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/d3b27439356b/CMMM2021-4348910.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/06d47561b4eb/CMMM2021-4348910.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/1bcb9bf9aec8/CMMM2021-4348910.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/bd5591d2f409/CMMM2021-4348910.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/d19f926cdc8b/CMMM2021-4348910.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/863398028117/CMMM2021-4348910.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/2d696ace0ed3/CMMM2021-4348910.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/186770ef7811/CMMM2021-4348910.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/4ee330cb3213/CMMM2021-4348910.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/b63b178718df/CMMM2021-4348910.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/82b1028703dd/CMMM2021-4348910.014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/f2cd247050e2/CMMM2021-4348910.015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/b0f93eaea828/CMMM2021-4348910.016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/c377458e21ba/CMMM2021-4348910.017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8db/8545521/4bdac8cf1f17/CMMM2021-4348910.alg.001.jpg

相似文献

1
Optimal Allocation of Vaccine and Antiviral Drugs for Influenza Containment over Delayed Multiscale Epidemic Model considering Time-Dependent Transmission Rate.考虑时变传播率的延迟多尺度传染病模型中流感防控的疫苗和抗病毒药物的最优分配
Comput Math Methods Med. 2021 Oct 18;2021:4348910. doi: 10.1155/2021/4348910. eCollection 2021.
2
Optimal control and sensitivity analysis of an influenza model with treatment and vaccination.具有治疗和疫苗接种的流感模型的最优控制与灵敏度分析
Acta Biotheor. 2011 Mar;59(1):1-28. doi: 10.1007/s10441-010-9095-8. Epub 2010 Feb 7.
3
Optimal control of vaccination dynamics during an influenza epidemic.流感流行期间疫苗接种动态的最优控制。
Math Biosci Eng. 2014 Oct;11(5):1045-63. doi: 10.3934/mbe.2014.11.1045.
4
A note on the use of optimal control on a discrete time model of influenza dynamics.关于在流感动力学离散时间模型中使用最优控制的注释。
Math Biosci Eng. 2011 Jan;8(1):183-97. doi: 10.3934/mbe.2011.8.183.
5
Observer-based adaptive PI sliding mode control of developed uncertain SEIAR influenza epidemic model considering dynamic population.基于观测器的自适应 PI 滑模控制考虑动态人口的发展不确定 SEIAR 流感传播模型。
J Theor Biol. 2019 Dec 7;482:109984. doi: 10.1016/j.jtbi.2019.08.015. Epub 2019 Aug 23.
6
Optimized strategy for the control and prevention of newly emerging influenza revealed by the spread dynamics model.传播动力学模型揭示的新型流感防控优化策略
PLoS One. 2014 Jan 2;9(1):e84694. doi: 10.1371/journal.pone.0084694. eCollection 2014.
7
A model for influenza with vaccination and antiviral treatment.一种包含疫苗接种和抗病毒治疗的流感模型。
J Theor Biol. 2008 Jul 7;253(1):118-30. doi: 10.1016/j.jtbi.2008.02.026. Epub 2008 Feb 26.
8
Antiviral drugs in influenza: an adjunct to vaccination in some situations.流感抗病毒药物:在某些情况下作为疫苗接种的辅助手段。
Prescrire Int. 2006 Feb;15(81):21-30.
9
Application of Optimal Control to Influenza Pneumonia Coinfection with Antiviral Resistance.最优控制在抗病毒耐药流感肺炎合并感染中的应用。
Comput Math Methods Med. 2020 Mar 10;2020:5984095. doi: 10.1155/2020/5984095. eCollection 2020.
10
Modeling the effects of vaccination and treatment on pandemic influenza.建模疫苗接种和治疗对大流行性流感的影响。
AAPS J. 2011 Sep;13(3):427-37. doi: 10.1208/s12248-011-9284-7. Epub 2011 Jun 8.

本文引用的文献

1
Transmission dynamics of novel coronavirus SARS-CoV-2 among healthcare workers, a case study in Iran.新型冠状病毒SARS-CoV-2在医护人员中的传播动态:伊朗的一个案例研究
Nonlinear Dyn. 2021;105(4):3749-3761. doi: 10.1007/s11071-021-06778-5. Epub 2021 Aug 10.
2
Parameter Estimation and Prediction of COVID-19 Epidemic Turning Point and Ending Time of a Case Study on SIR/SQAIR Epidemic Models.基于SIR/SQAIR传染病模型的新冠肺炎疫情转折点及结束时间的参数估计与预测——以某案例研究为例
Comput Math Methods Med. 2020 Dec 27;2020:1465923. doi: 10.1155/2020/1465923. eCollection 2020.
3
A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing.
一个具有明确易感染人群和无症状隔离舱的新型 COVID-19 传染病模型揭示了时机性社会隔离的意外后果。
J Theor Biol. 2021 Feb 7;510:110539. doi: 10.1016/j.jtbi.2020.110539. Epub 2020 Nov 24.
4
The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy.将人口迁移引入用于新冠疫情建模的SEIAR模型,并采用高效干预策略。
Inf Fusion. 2020 Dec;64:252-258. doi: 10.1016/j.inffus.2020.08.002. Epub 2020 Aug 6.
5
Optimal quarantine strategies for the COVID-19 pandemic in a population with a discrete age structure.针对具有离散年龄结构人群的新冠疫情的最优隔离策略。
Chaos Solitons Fractals. 2020 Nov;140:110166. doi: 10.1016/j.chaos.2020.110166. Epub 2020 Aug 13.
6
Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study.通过案例研究,运用最优控制分析对非药物干预措施对新型冠状病毒动态的影响进行建模。
Chaos Solitons Fractals. 2020 Oct;139:110075. doi: 10.1016/j.chaos.2020.110075. Epub 2020 Jul 3.
7
Optimal Control Design of Impulsive SQEIAR Epidemic Models with Application to COVID-19.具有脉冲效应的 SQEIAR 传染病模型的最优控制设计及其在 COVID-19 中的应用
Chaos Solitons Fractals. 2020 Oct;139:110054. doi: 10.1016/j.chaos.2020.110054. Epub 2020 Jun 30.
8
Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control.具有最优控制的HIV流行传播分数阶模型的全局动力学
Chaos Solitons Fractals. 2020 Sep;138:109826. doi: 10.1016/j.chaos.2020.109826. Epub 2020 Jun 18.
9
Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy.对意大利 COVID-19 疫情的建模与全民干预措施的实施。
Nat Med. 2020 Jun;26(6):855-860. doi: 10.1038/s41591-020-0883-7. Epub 2020 Apr 22.
10
Observer-based adaptive PI sliding mode control of developed uncertain SEIAR influenza epidemic model considering dynamic population.基于观测器的自适应 PI 滑模控制考虑动态人口的发展不确定 SEIAR 流感传播模型。
J Theor Biol. 2019 Dec 7;482:109984. doi: 10.1016/j.jtbi.2019.08.015. Epub 2019 Aug 23.