An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate
This study proposes an optimal control model for COVID-19 spread, incorporating a logistic recruitment rate. The observations show the disease-free equilibrium exists when the population-existing threshold exceeds 1. The stability of equilibrium is determined by the basic reproduction number R0. Thi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442524000777 |
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| author | Jonner Nainggolan Moch. Fandi Ansori Hengki Tasman |
| author_facet | Jonner Nainggolan Moch. Fandi Ansori Hengki Tasman |
| author_sort | Jonner Nainggolan |
| collection | DOAJ |
| description | This study proposes an optimal control model for COVID-19 spread, incorporating a logistic recruitment rate. The observations show the disease-free equilibrium exists when the population-existing threshold exceeds 1. The stability of equilibrium is determined by the basic reproduction number R0. This implies that equilibrium is stable when R0 is less than or equal to 1, but it is unstable when the value is greater than 1. Furthermore, an endemic equilibrium and stability is recorded when R0 exceeds 1. To identify influential factors in COVID-19 spread, sensitivity index and sensitivity analyses of R0 are conducted. The model perfectly integrates both prevention and therapy controls. As a result, numerical simulations show that the prevention control is more effective than the treatment control in reducing COVID-19 spread. Moreover, the simultaneous implementation of prevention and treatment controls outperforms individual control methods in mitigating COVID-19 spread. Finally, sensitivity analysis conducted with constant controls shows the contributions of the controls to disease dynamics. |
| format | Article |
| id | doaj-art-3c8ebd668e7f4a40bff7f4cc0f40a7b4 |
| institution | Kabale University |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-3c8ebd668e7f4a40bff7f4cc0f40a7b42025-01-19T06:26:56ZengElsevierHealthcare Analytics2772-44252025-06-017100375An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rateJonner Nainggolan0Moch. Fandi Ansori1Hengki Tasman2Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Cenderawasih, Jayapura, 99224, Indonesia; Corresponding author.Department of Mathematics, Faculty of Science and Mathematics, Universitas Diponegoro, Semarang, 50275, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Jakarta, 16424, IndonesiaThis study proposes an optimal control model for COVID-19 spread, incorporating a logistic recruitment rate. The observations show the disease-free equilibrium exists when the population-existing threshold exceeds 1. The stability of equilibrium is determined by the basic reproduction number R0. This implies that equilibrium is stable when R0 is less than or equal to 1, but it is unstable when the value is greater than 1. Furthermore, an endemic equilibrium and stability is recorded when R0 exceeds 1. To identify influential factors in COVID-19 spread, sensitivity index and sensitivity analyses of R0 are conducted. The model perfectly integrates both prevention and therapy controls. As a result, numerical simulations show that the prevention control is more effective than the treatment control in reducing COVID-19 spread. Moreover, the simultaneous implementation of prevention and treatment controls outperforms individual control methods in mitigating COVID-19 spread. Finally, sensitivity analysis conducted with constant controls shows the contributions of the controls to disease dynamics.http://www.sciencedirect.com/science/article/pii/S2772442524000777Optimal control modelLogistic recruitment rateBasic reproduction numberCOVID-19Sensitivity analysis |
| spellingShingle | Jonner Nainggolan Moch. Fandi Ansori Hengki Tasman An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate Healthcare Analytics Optimal control model Logistic recruitment rate Basic reproduction number COVID-19 Sensitivity analysis |
| title | An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate |
| title_full | An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate |
| title_fullStr | An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate |
| title_full_unstemmed | An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate |
| title_short | An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate |
| title_sort | optimal control model with sensitivity analysis for covid 19 transmission using logistic recruitment rate |
| topic | Optimal control model Logistic recruitment rate Basic reproduction number COVID-19 Sensitivity analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2772442524000777 |
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