J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (3): 545-554.doi: 10.1007/s12204-023-2642-7

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基于粒可微性印度COVID-19疫情模糊动态最优模型

  

  1. 1. KG Reddy College of Engineering and Technology, Hyderabad, Telangana 500075, India; 2. Haldia Institute of Technology, Haldia 721657, India; 3. National Institute of Technology, Durgapur 713209, India; 4. Global Institute of Science & Technology, Haldia 721657, India; 5. XIM University, Bhubaneswar 751013, India; 6. Indian Institute of Technology, Kharagpur 721302, India
  • 收稿日期:2022-07-25 接受日期:2022-12-23 出版日期:2025-06-06 发布日期:2025-06-06

Fuzzy Dynamic Optimal Model for COVID-19 Epidemic in India Based on Granular Differentiability

KHATUA Debnarayan1, DE Anupam2, KAR Samarjit3, SAMANTA Eshan4, SEKH Arif Ahmed5, GUHA ADHYA Debashree6   

  1. 1. KG Reddy College of Engineering and Technology, Hyderabad, Telangana 500075, India; 2. Haldia Institute of Technology, Haldia 721657, India; 3. National Institute of Technology, Durgapur 713209, India; 4. Global Institute of Science & Technology, Haldia 721657, India; 5. XIM University, Bhubaneswar 751013, India; 6. Indian Institute of Technology, Kharagpur 721302, India
  • Received:2022-07-25 Accepted:2022-12-23 Online:2025-06-06 Published:2025-06-06

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摘要： 流行病毒SARS-CoV-2，命名为COVID-19，未来几年可能持续大规模传播。由于还未获得可证实的药物，死亡率迅速上升。保护健康的首要措施是病人或感染区域隔离。由于人类是唯一携带者，如果将感染人群或病毒携带者相互隔离，可能会控制阳性率。隔离可能不是一个适当的解决办法，这些是当前在世界各地开展的COVID-19研究工作共识。因为在世界各地的数据库和印度每天记录的阳性病例不精确，目前世界和公共卫生情况正在受到COVID-19严重不确定性问题的打击。在这项研究中，我们提出了一种基于模糊动态系统的粒可微性COVID-19最优控制模型。第一步，我们创建了COVID-19模糊易感-暴露-感染-无症状-住院-康复-死亡（SEIAHRD）模型，使用粒可微性对其进行分析，并报告了与时间无关疾病控制参数的疾病动态。第二步，我们将与时间相关疾病控制参数相关联的模糊动力系统和粒可微性模型升级为最优控制问题输入。用第一波和第二波初期印度视角的新冠肺炎疫情相关实际数据对理论研究进行了验证。

关键词: COVID-19, 无症状, 易感, 模糊动力系统, 粒可微性

Abstract: The pandemic SARS-CoV-2 has become an undying virus to spread a sustainable disease named COVID-19 for upcoming few years. Mortality rates are rising rapidly as approved drugs are not yet available. Isolation from the infected person or community is the preferred choice to protect our health. Since humans are the only carriers, it might be possible to control the positive rate if the infected population or host carriers are isolated from each other. Isolation alone may not be a proper solution. These are the resolutions of previous research work carried out on COVID-19 throughout the world. The present scenario of the world and public health is knocking hard with a big question of critical uncertainty of COVID-19 because of its imprecise database as per daily positive cases recorded all over the world and in India as well. In this research work, we have presented an optimal control model for COVID-19 using granular differentiability based on fuzzy dynamical systems. In the first step, we created a fuzzy Susceptible-Exposed-Infected-Asymptomatic-Hospitalized-Recovered-Death (SEIAHRD) model for COVID-19, analyzed it using granular differentiability, and reported disease dynamics for time-independent disease control parameters. In the second step, we upgraded the fuzzy dynamical system and granular differentiability model related to time-dependent disease control parameters as an optimal control problem invader. Theoretical studies have been validated with some practical data from the epidemic COVID-19 related to the Indian perspective during first wave and early second wave.

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