Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control?

IF 2.3 Q3 ENGINEERING, BIOMEDICAL

Biomedical Engineering and Computational Biology Pub Date : 2025-04-16 eCollection Date: 2025-01-01 DOI:10.1177/11795972251321306

Mohadeseh Najafi, Hamidreza Mortazavy Beni, Ashkan Heydarian, Samaneh Sadat Sajjadi, Ahmad Hajipour

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Abstract

Although SARS-COV-2 started in 2019, its losses are still significant, and it takes victims. In the present study, the epidemic patterns of SARS-COV-2 disease have been investigated from the point of view of mathematical modeling. Also, the effect of quarantine has been considered. This mathematical model is designed in the form of fractional calculations along with a model predictive control (MPC) to monitor this model. The fractional-order model has the memory and hereditary properties of the system, which can provide more adjustable parameters to the designer. Because the MPC can predict future outputs, it can overcome the conditions and events that occur in the future. The results of the simulations show that the proposed nonlinear model predictive controller (NMPC) of fractional-order has a lower mean squared error in susceptible people compared to the optimal control of fractional-order (~3.6e-04 vs. 47.4). This proposed NMPC of fractional-order can be used for other models of epidemics.

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考虑非线性预测控制是否遏制了SARS-CoV-2变体的传播？

尽管SARS-COV-2始于2019年，但它的损失仍然很大，而且会造成受害者。本研究从数学建模的角度研究了SARS-COV-2疾病的流行模式。此外，还考虑了隔离的影响。该数学模型以分数计算的形式设计，并采用模型预测控制（MPC）来监控该模型。分数阶模型具有系统的记忆性和遗传性，可以为设计人员提供更多的可调参数。由于MPC可以预测未来的输出，它可以克服未来发生的条件和事件。仿真结果表明，与分数阶最优控制相比，分数阶非线性模型预测控制器（NMPC）在易感人群中的均方误差更低（~3.6e-04 vs. 47.4）。提出的分数阶NMPC可用于其他流行病模型。

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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Biomedical Engineering and Computational Biology ENGINEERING, BIOMEDICAL-

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8 weeks