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埃博拉病毒扩散最优控制系统研究
Study on Optimal Control System of Ebola Disease

DOI: 10.12677/SSEM.2016.52006, PP. 48-66

Keywords: 埃博拉病毒,概率模型,病程发展模型,系统动力学模型
Ebola Virus
, Probability Model, Disease Development Course Model, System Dynamics Model

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Abstract:

埃博拉病毒是一种致命性的传染性疾病,严重危及人类的生命安全。准确预测传染病的发展趋势,对于及时配置和调集相关医疗资源、制定合理应对策略,解决卫生部门在应急处理中的经验误区有至关重要的意义。为有效控制该病毒的扩散,本文采用不同的方法建立埃博拉病毒传播预测模型,分别包括概率模型、病程发展模型和系统动力学模型。通过对该病毒的扩散历史中相关时序变量进行研究,寻找其传播规律,构建埃博拉传染病预测模型,可以对该病毒的未来传播情况做出预测,并对不同防控干预措施进行模拟仿真,检测不同措施的效果,为疫情区所在国家制定有效应对方案,健全公共卫生体系,为相关部门提高应急管理能力提供技术支撑和相关建议。此外,本文所构建的模型将一些现实影响因素,如药品配送难易程度,疫情区卫生服务水平等因素考虑在内,对模型进行改进,从而更为真实的模拟埃博拉病毒的蔓延扩散过程。
Ebola virus is a deadly infectious disease, which seriously endangers human life and safety. Ac-curate prediction of the development trend of an infectious disease is of significant importance for the timely allocation and mobilization of relevant medical resources, developing reasonable strategies, as well as solving the problem of experience dependence in emergency treatment of the health sector. In order to effectively deal with the spread of the virus, this paper introduces three different methods to establish the prediction model of the Ebola virus transmission process, i.e. the probability model, the disease development course model and the system dynamics model. Through data mining of the time series variables in the disease spreading history, we can establish our predicting model based on the spreading rules of Ebola epidemic disease. Through the Ebola epidemic prediction model, we can make predictions on the future spread of the virus and simulate different control strategies under different scenarios to explore for the most effective measures. Therefore, through this research, we can provide effective technical support and suggestions for epidemic countries in Ebola disease management and to improve the public health emergency response ability. In addition, the proposed models of this paper also take some realistic factors, such as the difficulty degree of drug distribution and the medical service level in different epidemic areas into account to make improvements on the original model, which help the models to be more realistic in the simulation of the Ebola virus spreading process.

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