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通过考虑非线性预测控制来遏制SARS-CoV-2变体的传播？

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

作者信息

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

机构信息

Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.

Department of Biomedical Engineering, Arsanjan Branch, Islamic Azad University, Arsanjan, Iran.

出版信息

Biomed Eng Comput Biol. 2025 Apr 16;16:11795972251321306. doi: 10.1177/11795972251321306. eCollection 2025.

DOI:10.1177/11795972251321306
PMID:40290618
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12033554/
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.

摘要

尽管新冠病毒于2019年出现，但其造成的损失仍然巨大，且仍有受害者。在本研究中，从数学建模的角度对新冠病毒疾病的流行模式进行了调查。此外，还考虑了隔离的效果。该数学模型采用分数阶计算形式，并结合模型预测控制（MPC）来监测此模型。分数阶模型具有系统的记忆和遗传特性，可为设计者提供更多可调整参数。由于MPC可以预测未来输出，因此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，所提出的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（约3.6e-04对47.4）。所提出的这种分数阶NMPC可用于其他流行病模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/d228dcb7f56b/10.1177_11795972251321306-fig19.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/cb74be894693/10.1177_11795972251321306-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/d15a750a2153/10.1177_11795972251321306-fig2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/5472103b7e44/10.1177_11795972251321306-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/5f5ae4111d31/10.1177_11795972251321306-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/e7a11a9800b3/10.1177_11795972251321306-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/4ac065c36a97/10.1177_11795972251321306-fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/50cdf61c8b17/10.1177_11795972251321306-fig12.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/51adc733694e/10.1177_11795972251321306-fig14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/470cc55962a3/10.1177_11795972251321306-fig15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/ee93d2b666f0/10.1177_11795972251321306-fig16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/0910528b957c/10.1177_11795972251321306-fig17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aec/12033554/02a8da7a8623/10.1177_11795972251321306-fig18.jpg
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