Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control?
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...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Biomedical Engineering and Computational Biology |
| Online Access: | https://doi.org/10.1177/11795972251321306 |
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| author | Mohadeseh Najafi Hamidreza Mortazavy Beni Ashkan Heydarian Samaneh Sadat Sajjadi Ahmad Hajipour |
| author_facet | Mohadeseh Najafi Hamidreza Mortazavy Beni Ashkan Heydarian Samaneh Sadat Sajjadi Ahmad Hajipour |
| author_sort | Mohadeseh Najafi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a7e811643feb46ccb72652e892980ae6 |
| institution | DOAJ |
| issn | 1179-5972 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Biomedical Engineering and Computational Biology |
| spelling | doaj-art-a7e811643feb46ccb72652e892980ae62025-08-20T03:18:13ZengSAGE PublishingBiomedical Engineering and Computational Biology1179-59722025-04-011610.1177/11795972251321306Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control?Mohadeseh Najafi0Hamidreza Mortazavy Beni1Ashkan Heydarian2Samaneh Sadat Sajjadi3Ahmad Hajipour4Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, IranDepartment of Biomedical Engineering, Arsanjan Branch, Islamic Azad University, Arsanjan, IranDepartment of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, IranDepartment of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, IranAlthough 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.https://doi.org/10.1177/11795972251321306 |
| spellingShingle | Mohadeseh Najafi Hamidreza Mortazavy Beni Ashkan Heydarian Samaneh Sadat Sajjadi Ahmad Hajipour Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control? Biomedical Engineering and Computational Biology |
| title | Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control? |
| title_full | Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control? |
| title_fullStr | Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control? |
| title_full_unstemmed | Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control? |
| title_short | Is it Curbing-spread of SARS-CoV-2 Variants by Considering Non-linear Predictive Control? |
| title_sort | is it curbing spread of sars cov 2 variants by considering non linear predictive control |
| url | https://doi.org/10.1177/11795972251321306 |
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