A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control
Abstract This paper presents a comparative analysis of deterministic and stochastic computational modeling approaches for the optimal control of COVID-19. We formulate a compartmental epidemic model with perturbation by white noise that incorporates various factors influencing disease transmission....
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-96127-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850269827714777088 |
|---|---|
| author | Abdeldjalil Kadri Ahmed Boudaoui Saif Ullah Mohammed Asiri Abdul Baseer Saqib Muhammad Bilal Riaz |
| author_facet | Abdeldjalil Kadri Ahmed Boudaoui Saif Ullah Mohammed Asiri Abdul Baseer Saqib Muhammad Bilal Riaz |
| author_sort | Abdeldjalil Kadri |
| collection | DOAJ |
| description | Abstract This paper presents a comparative analysis of deterministic and stochastic computational modeling approaches for the optimal control of COVID-19. We formulate a compartmental epidemic model with perturbation by white noise that incorporates various factors influencing disease transmission. By incorporating stochastic effects, the model accounts for uncertainties inherent in real-world epidemic data. We establish the mathematical properties of the model, such as well-posedness and the existence of stationary distributions, which are crucial for understanding long-term epidemic dynamics. Moreover, the study presents an optimal control strategies to mitigate the epidemic’s impact, both in deterministic and stochastic sceneries. Reported data from Algeria are used to parameterize the model, ensuring its relevance and applicability to practical satiation. Through numerical simulations, the study provides insights into the effectiveness of different control measures in managing COVID-19 outbreaks. This research contributes to advancing our understanding of epidemic dynamics and informs decision-making processes for epidemic controlling interventions. |
| format | Article |
| id | doaj-art-1165082bf9844a43ba1e34ad0f4e921e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1165082bf9844a43ba1e34ad0f4e921e2025-08-20T01:52:55ZengNature PortfolioScientific Reports2045-23222025-04-0115112210.1038/s41598-025-96127-yA comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 controlAbdeldjalil Kadri0Ahmed Boudaoui1Saif Ullah2Mohammed Asiri3Abdul Baseer Saqib4Muhammad Bilal Riaz5Laboratory of Mathematics Modeling and Applications, University of AdrarLaboratory of Mathematics Modeling and Applications, University of AdrarDepartment of Mathematics, University of PeshawarDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid UniversityFaculty of Education, Nangrahar UniversityIT4Innovations, VSB-Technical University of OstravaAbstract This paper presents a comparative analysis of deterministic and stochastic computational modeling approaches for the optimal control of COVID-19. We formulate a compartmental epidemic model with perturbation by white noise that incorporates various factors influencing disease transmission. By incorporating stochastic effects, the model accounts for uncertainties inherent in real-world epidemic data. We establish the mathematical properties of the model, such as well-posedness and the existence of stationary distributions, which are crucial for understanding long-term epidemic dynamics. Moreover, the study presents an optimal control strategies to mitigate the epidemic’s impact, both in deterministic and stochastic sceneries. Reported data from Algeria are used to parameterize the model, ensuring its relevance and applicability to practical satiation. Through numerical simulations, the study provides insights into the effectiveness of different control measures in managing COVID-19 outbreaks. This research contributes to advancing our understanding of epidemic dynamics and informs decision-making processes for epidemic controlling interventions.https://doi.org/10.1038/s41598-025-96127-yCOVID-19 stochastic modelingExtinctionStationary distributionStochastic optimized controlSimulation |
| spellingShingle | Abdeldjalil Kadri Ahmed Boudaoui Saif Ullah Mohammed Asiri Abdul Baseer Saqib Muhammad Bilal Riaz A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control Scientific Reports COVID-19 stochastic modeling Extinction Stationary distribution Stochastic optimized control Simulation |
| title | A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control |
| title_full | A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control |
| title_fullStr | A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control |
| title_full_unstemmed | A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control |
| title_short | A comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing COVID-19 control |
| title_sort | comparative study of deterministic and stochastic computational modeling approaches for analyzing and optimizing covid 19 control |
| topic | COVID-19 stochastic modeling Extinction Stationary distribution Stochastic optimized control Simulation |
| url | https://doi.org/10.1038/s41598-025-96127-y |
| work_keys_str_mv | AT abdeldjalilkadri acomparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT ahmedboudaoui acomparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT saifullah acomparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT mohammedasiri acomparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT abdulbaseersaqib acomparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT muhammadbilalriaz acomparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT abdeldjalilkadri comparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT ahmedboudaoui comparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT saifullah comparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT mohammedasiri comparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT abdulbaseersaqib comparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control AT muhammadbilalriaz comparativestudyofdeterministicandstochasticcomputationalmodelingapproachesforanalyzingandoptimizingcovid19control |