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针对感染人群的结核病的状态反馈和协同控制器。

State Feedback and Synergetic controllers for tuberculosis in infected population.

机构信息

School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan.

出版信息

IET Syst Biol. 2021 May;15(3):83-92. doi: 10.1049/syb2.12013. Epub 2021 Mar 30.

DOI:10.1049/syb2.12013
PMID:33786984
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8675849/
Abstract

Tuberculosis (TB) is a contagious disease which can easily be disseminated in a society. A five state Susceptible, exposed, infected, recovered and resistant (SEIRs) epidemiological mathematical model of TB has been considered along with two non-linear controllers: State Feedback (SFB) and Synergetic controllers have been designed for the control and prevention of the TB in a population. Using the proposed controllers, the infected individuals have been reduced/controlled via treatment, and susceptible individuals have been prevented from the disease via vaccination. A mathematical analysis has been carried out to prove the asymptotic stability of proposed controllers by invoking the Lyapunov control theory. Simulation results using MATLAB/Simulink manifest that the non-linear controllers show fast convergence of the system states to their respective desired levels. Comparison shows that proposed SFB controller performs better than Synergetic controller in terms of convergence time, steady state error and oscillations.

摘要

结核病（TB）是一种传染性疾病，在社会中很容易传播。本文考虑了一个五状态易感染、暴露、感染、恢复和抗性（SEIRs）的结核病流行病学数学模型，并设计了两种非线性控制器：状态反馈（SFB）和协同控制器，用于控制和预防人群中的结核病。使用所提出的控制器，通过治疗来减少/控制感染个体，并通过疫苗接种来预防易感个体患病。通过调用 Lyapunov 控制理论，进行了数学分析以证明所提出的控制器的渐近稳定性。使用 MATLAB/Simulink 的仿真结果表明，非线性控制器使系统状态快速收敛到各自的期望水平。比较表明，在收敛时间、稳态误差和振荡方面，所提出的 SFB 控制器比协同控制器表现更好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/609512f8ebfe/SYB2-15-83-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/5a4f5499d6ec/SYB2-15-83-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/d2ded4c2f0e5/SYB2-15-83-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/ac656b7e978d/SYB2-15-83-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/ae0f81e651ff/SYB2-15-83-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/609512f8ebfe/SYB2-15-83-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/5a4f5499d6ec/SYB2-15-83-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/ab4d032f95b3/SYB2-15-83-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/9bd7890a8fde/SYB2-15-83-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/246753e12c5d/SYB2-15-83-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/9f10fe3bd312/SYB2-15-83-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/d2ded4c2f0e5/SYB2-15-83-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/ac656b7e978d/SYB2-15-83-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/ae0f81e651ff/SYB2-15-83-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f5/8675849/609512f8ebfe/SYB2-15-83-g005.jpg

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本文引用的文献

1
Optimal control analysis of a tuberculosis model.结核病模型的最优控制分析
Appl Math Model. 2018 Jun;58:47-64. doi: 10.1016/j.apm.2017.12.027. Epub 2017 Dec 29.
2
What We Know About Tuberculosis Transmission: An Overview.我们对结核病传播的了解：概述
J Infect Dis. 2017 Nov 3;216(suppl_6):S629-S635. doi: 10.1093/infdis/jix362.
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Tuberculosis: current situation, challenges and overview of its control programs in India.结核病：印度的现状、挑战及其防控项目概述
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J Theor Biol. 2011 Jun 21;279(1):120-31. doi: 10.1016/j.jtbi.2011.03.009. Epub 2011 Mar 30.
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Tuberculosis surveillance by analyzing Google trends.基于谷歌趋势分析的结核病监测
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Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales.通过追踪非处方药物销售情况对炭疽疫情进行早期统计检测。
Proc Natl Acad Sci U S A. 2002 Apr 16;99(8):5237-40. doi: 10.1073/pnas.042117499.