Dynamic analysis and optimal control of a class of SISP respiratory diseases.

IF 1.8 4区 数学 Q3 ECOLOGY
Lei Shi, Longxing Qi
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引用次数: 4

Abstract

In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM2.5 is taken into account. The concentration response function of PM2.5 is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission P0 of PM2.5 and PM2.5 pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM2.5 by reducing the PM2.5 emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients.

一类SISP呼吸系统疾病的动态分析与最优控制。
本文考虑了易感人群在吸入一定量PM2.5后直接发病的实际背景。引入PM2.5浓度响应函数,提出了SISP呼吸系统疾病模型。定性理论分析证明,平衡点的存在性、局部稳定性和全局稳定性均与PM2.5日排放P0和PM2.5致病阈值k有关。基于敏感性因子分析和参数对患者数量的时变敏感性分析,发现转化率β和吸入率η具有最大的正相关性。感染者治愈率γ与患者人数负相关最大。根据最优控制理论的分析结果制定的控制策略如下:第一步通过减少PM2.5的排放和加大除尘强度来提高PM2.5的清除率。而且,这种拆除工作必须长期维护。第二步是通过及时治疗来提高患者的治愈率。之后要提醒市民佩戴口罩,减少外出,减少易感人群成为患者的转换率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biological Dynamics
Journal of Biological Dynamics ECOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.90
自引率
3.60%
发文量
28
审稿时长
33 weeks
期刊介绍: Journal of Biological Dynamics, an open access journal, publishes state of the art papers dealing with the analysis of dynamic models that arise from biological processes. The Journal focuses on dynamic phenomena at scales ranging from the level of individual organisms to that of populations, communities, and ecosystems in the fields of ecology and evolutionary biology, population dynamics, epidemiology, immunology, neuroscience, environmental science, and animal behavior. Papers in other areas are acceptable at the editors’ discretion. In addition to papers that analyze original mathematical models and develop new theories and analytic methods, the Journal welcomes papers that connect mathematical modeling and analysis to experimental and observational data. The Journal also publishes short notes, expository and review articles, book reviews and a section on open problems.
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