Dynamics of COVID-19 Using SEIQR Epidemic Model
The major goal of this study is to create an optimal technique for managing COVID-19 spread by transforming the SEIQR model into a dynamic (multistage) programming problem with continuous and discrete time-varying transmission rates as optimizing variables. We have developed an optimal control probl...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/2138165 |
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| _version_ | 1832562308954456064 |
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| author | N. Avinash G. Britto Antony Xavier Ammar Alsinai Hanan Ahmed V. Rexma Sherine P. Chellamani |
| author_facet | N. Avinash G. Britto Antony Xavier Ammar Alsinai Hanan Ahmed V. Rexma Sherine P. Chellamani |
| author_sort | N. Avinash |
| collection | DOAJ |
| description | The major goal of this study is to create an optimal technique for managing COVID-19 spread by transforming the SEIQR model into a dynamic (multistage) programming problem with continuous and discrete time-varying transmission rates as optimizing variables. We have developed an optimal control problem for a discrete-time, deterministic susceptible class (S), exposed class (E), infected class (I), quarantined class (Q), and recovered class (R) epidemic with a finite time horizon. The problem involves finding the minimum objective function of a controlled process subject to the constraints of limited resources. For our model, we present a new technique based on dynamic programming problem solutions that can be used to minimize infection rate and maximize recovery rate. We developed suitable conditions for obtaining monotonic solutions and proposed a dynamic programming model to obtain optimal transmission rate sequences. We explored the positivity and unique solvability nature of these implicit and explicit time-discrete models. According to our findings, isolating the affected humans can limit the danger of COVID-19 spreading in the future. |
| format | Article |
| id | doaj-art-1f29166592194d308eff63fea36eaa84 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-1f29166592194d308eff63fea36eaa842025-02-03T01:22:54ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/2138165Dynamics of COVID-19 Using SEIQR Epidemic ModelN. Avinash0G. Britto Antony Xavier1Ammar Alsinai2Hanan Ahmed3V. Rexma Sherine4P. Chellamani5Department of MathematicsDepartment of MathematicsDepartment of Studies in MathematicsDepartment of MathematicsDepartment of MathematicsDepartment of MathematicsThe major goal of this study is to create an optimal technique for managing COVID-19 spread by transforming the SEIQR model into a dynamic (multistage) programming problem with continuous and discrete time-varying transmission rates as optimizing variables. We have developed an optimal control problem for a discrete-time, deterministic susceptible class (S), exposed class (E), infected class (I), quarantined class (Q), and recovered class (R) epidemic with a finite time horizon. The problem involves finding the minimum objective function of a controlled process subject to the constraints of limited resources. For our model, we present a new technique based on dynamic programming problem solutions that can be used to minimize infection rate and maximize recovery rate. We developed suitable conditions for obtaining monotonic solutions and proposed a dynamic programming model to obtain optimal transmission rate sequences. We explored the positivity and unique solvability nature of these implicit and explicit time-discrete models. According to our findings, isolating the affected humans can limit the danger of COVID-19 spreading in the future.http://dx.doi.org/10.1155/2022/2138165 |
| spellingShingle | N. Avinash G. Britto Antony Xavier Ammar Alsinai Hanan Ahmed V. Rexma Sherine P. Chellamani Dynamics of COVID-19 Using SEIQR Epidemic Model Journal of Mathematics |
| title | Dynamics of COVID-19 Using SEIQR Epidemic Model |
| title_full | Dynamics of COVID-19 Using SEIQR Epidemic Model |
| title_fullStr | Dynamics of COVID-19 Using SEIQR Epidemic Model |
| title_full_unstemmed | Dynamics of COVID-19 Using SEIQR Epidemic Model |
| title_short | Dynamics of COVID-19 Using SEIQR Epidemic Model |
| title_sort | dynamics of covid 19 using seiqr epidemic model |
| url | http://dx.doi.org/10.1155/2022/2138165 |
| work_keys_str_mv | AT navinash dynamicsofcovid19usingseiqrepidemicmodel AT gbrittoantonyxavier dynamicsofcovid19usingseiqrepidemicmodel AT ammaralsinai dynamicsofcovid19usingseiqrepidemicmodel AT hananahmed dynamicsofcovid19usingseiqrepidemicmodel AT vrexmasherine dynamicsofcovid19usingseiqrepidemicmodel AT pchellamani dynamicsofcovid19usingseiqrepidemicmodel |