Modeling and control of COVID-19 disease using deep reinforcement learning method.

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nazanin Ghazizadeh, Sajjad Taghvaei, Seyyed Arash Haghpanah
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引用次数: 0

Abstract

The prevalence of epidemics has been studied by researchers in various fields. In the last 2 years, the outbreak of COVID-19 has affected the health, economy, and industry of communities around the world and has caused the death of millions of people. Therefore, many researchers have tried to model and control the prevalence of this disease. In this article, the new SQEIAR model for the spread of the COVID-19 disease is provided, which, compared to previous models, explores the effects of additional interventions on the outbreak and incorporates a wider range of variables and parameters to enhance its accuracy and alignment with reality. These modifications in the model lead to a more rapid eradication and control of the disease. This model includes six variables of the group of susceptible, quarantined, exposed, symptomatic, asymptomatic, and recovered individuals and includes three control inputs such as quarantine of susceptible, vaccination, and treatments. In order to minimize symptomatic infectious individuals and susceptible individuals and also to reduce treatment, vaccination, and quarantine costs, an optimal control approach using the Deep Deterministic Policy Gradient (DDPG) method has been applied to the system. This algorithm is applied to the model in different cases of control inputs, and for each case, optimal control inputs are obtained. In the following, the number of deaths due to the disease and the total number of symptomatic infectious individuals for each of these optimal control cases has been calculated. The results of the implemented control structure demonstrated a reduction of 60% in the number of deaths and 74% in the number of symptomatically infected individuals compared to the uncontrolled model. Finally, to test the performance of the control system, noise was applied to the system in various ways, including three methods: applying noise to observer variables, applying noise to control inputs, and applying uncertainty to model parameters. Therefore, we found that this control system was robust and performed well in different conditions despite the disturbance.

Abstract Image

利用深度强化学习方法对 COVID-19 疾病进行建模和控制。
各领域的研究人员一直在研究流行病的流行情况。在过去两年中,COVID-19 的爆发影响了世界各地社区的健康、经济和工业,并造成数百万人死亡。因此,许多研究人员试图模拟和控制这种疾病的流行。本文提供了 COVID-19 疾病传播的新 SQEIAR 模型,与以前的模型相比,该模型探讨了额外干预措施对疾病爆发的影响,并纳入了更广泛的变量和参数,以提高其准确性和与现实的一致性。对模型的这些修改可更快地根除和控制疫情。该模型包括易感人群、隔离人群、暴露人群、有症状人群、无症状人群和康复人群六个变量,并包括易感人群隔离、疫苗接种和治疗等三个控制输入。为了最大限度地减少无症状感染者和易感人群,同时降低治疗、疫苗接种和检疫成本,该系统采用了深度确定性策略梯度法(DDPG)的最优控制方法。该算法适用于不同控制输入情况下的模型,并在每种情况下获得最佳控制输入。随后,计算了每种最佳控制情况下的疾病死亡人数和有症状的感染者总数。实施控制结构的结果表明,与不受控制的模型相比,死亡人数减少了 60%,有症状的感染者人数减少了 74%。最后,为了测试控制系统的性能,我们以不同的方式对系统施加了噪声,包括三种方法:对观测变量施加噪声、对控制输入施加噪声以及对模型参数施加不确定性。因此,我们发现该控制系统具有鲁棒性,在不同条件下均表现良好,尽管存在干扰。
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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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