Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model
In this study, a new computing technique is introduced to solve the susceptible-exposed-infected-and-recovery (SEIR) Ebola virus model represented with the system of ordinary differential equations through Levenberg–Marquardt backpropagation neural networks. The dynamics of the SEIR model are examin...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2021-09-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0056196 |
| _version_ | 1818824390479970304 |
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| author | Tahir Nawaz Cheema Shafaq Naz |
| author_facet | Tahir Nawaz Cheema Shafaq Naz |
| author_sort | Tahir Nawaz Cheema |
| collection | DOAJ |
| description | In this study, a new computing technique is introduced to solve the susceptible-exposed-infected-and-recovery (SEIR) Ebola virus model represented with the system of ordinary differential equations through Levenberg–Marquardt backpropagation neural networks. The dynamics of the SEIR model are examined by the variation in different parameters, such as the increase in the susceptible rate while keeping other parameters fixed, such as the natural death rate of susceptibility, susceptible exposed rate, infected exposed rate, and infected to recovered rate; the four types of infected rates, namely, the natural mortality rate, rate of exposed death due to the disease, natural infected mortality rate, and rate of infected death due to the disease; and the rate of natural mortality of the recovered. The datasets for the SEIR nonlinear system for measuring the effects of Ebola virus disease spread dynamics are generated through the Runge–Kutta method for each scenario. The efficiency of the proposed computing technique—LMBNNs—is analyzed through absolute deviation, mean square error, learning curves, histogram analysis, and regression metrics, which provides a way for validation, testing, and training through the scheme. |
| first_indexed | 2024-12-18T23:55:07Z |
| format | Article |
| id | doaj.art-753a2e6075954fd2bc5498ecbce716e5 |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-12-18T23:55:07Z |
| publishDate | 2021-09-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-753a2e6075954fd2bc5498ecbce716e52022-12-21T20:46:43ZengAIP Publishing LLCAIP Advances2158-32262021-09-01119095205095205-1310.1063/5.0056196Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic modelTahir Nawaz Cheema0Shafaq Naz1Department of Mathematics, University of Gujarat, Gujarat 50700, PakistanDepartment of Mathematics, University of Gujarat, Gujarat 50700, PakistanIn this study, a new computing technique is introduced to solve the susceptible-exposed-infected-and-recovery (SEIR) Ebola virus model represented with the system of ordinary differential equations through Levenberg–Marquardt backpropagation neural networks. The dynamics of the SEIR model are examined by the variation in different parameters, such as the increase in the susceptible rate while keeping other parameters fixed, such as the natural death rate of susceptibility, susceptible exposed rate, infected exposed rate, and infected to recovered rate; the four types of infected rates, namely, the natural mortality rate, rate of exposed death due to the disease, natural infected mortality rate, and rate of infected death due to the disease; and the rate of natural mortality of the recovered. The datasets for the SEIR nonlinear system for measuring the effects of Ebola virus disease spread dynamics are generated through the Runge–Kutta method for each scenario. The efficiency of the proposed computing technique—LMBNNs—is analyzed through absolute deviation, mean square error, learning curves, histogram analysis, and regression metrics, which provides a way for validation, testing, and training through the scheme.http://dx.doi.org/10.1063/5.0056196 |
| spellingShingle | Tahir Nawaz Cheema Shafaq Naz Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model AIP Advances |
| title | Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model |
| title_full | Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model |
| title_fullStr | Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model |
| title_full_unstemmed | Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model |
| title_short | Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model |
| title_sort | numerical computing with levenberg marquardt backpropagation networks for nonlinear seir ebola virus epidemic model |
| url | http://dx.doi.org/10.1063/5.0056196 |
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