Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states
By using graph Laplacian diffusion, the susceptible-infected-removed (SIR) epidemic model is expanded to include a weighted network that exhibits randomness in the transmission rate parameter, representing population mobility between network nodes. Our goal is to estimate critical parameters, transm...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2436662 |
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| author | Prince Achankunju Saroj Kumar Dash |
| author_facet | Prince Achankunju Saroj Kumar Dash |
| author_sort | Prince Achankunju |
| collection | DOAJ |
| description | By using graph Laplacian diffusion, the susceptible-infected-removed (SIR) epidemic model is expanded to include a weighted network that exhibits randomness in the transmission rate parameter, representing population mobility between network nodes. Our goal is to estimate critical parameters, transmission rate (β), and recovery rate (γ), using the Joint Extended Kalman Filter (JEKF). This sophisticated estimation algorithm iteratively predicts and refines the state based on current estimates, combining model predictions with observed data to enhance accuracy, making it effective for estimating critical parameters in dynamic systems. Finally, we visually illustrate our algorithms through experiments conducted on a small-world Watts-Strogatz graph and a simple tree network. The conclusive results identify the best-fit parameters for effective COVID-19 management in India. |
| format | Article |
| id | doaj-art-dc2df774e23642e9befc958f35c80be2 |
| institution | DOAJ |
| issn | 2164-2583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-dc2df774e23642e9befc958f35c80be22025-08-20T02:49:30ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832024-12-0112110.1080/21642583.2024.2436662Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian statesPrince Achankunju0Saroj Kumar Dash1School of Advanced Sciences, Vellore Institute of Technology, Chennai, IndiaSchool of Advanced Sciences, Vellore Institute of Technology, Chennai, IndiaBy using graph Laplacian diffusion, the susceptible-infected-removed (SIR) epidemic model is expanded to include a weighted network that exhibits randomness in the transmission rate parameter, representing population mobility between network nodes. Our goal is to estimate critical parameters, transmission rate (β), and recovery rate (γ), using the Joint Extended Kalman Filter (JEKF). This sophisticated estimation algorithm iteratively predicts and refines the state based on current estimates, combining model predictions with observed data to enhance accuracy, making it effective for estimating critical parameters in dynamic systems. Finally, we visually illustrate our algorithms through experiments conducted on a small-world Watts-Strogatz graph and a simple tree network. The conclusive results identify the best-fit parameters for effective COVID-19 management in India.https://www.tandfonline.com/doi/10.1080/21642583.2024.2436662Graph Laplacian diffusionepidemic modeljoint extended Kalman filterstochastic perturbationsEuler-Maruyama method |
| spellingShingle | Prince Achankunju Saroj Kumar Dash Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states Systems Science & Control Engineering Graph Laplacian diffusion epidemic model joint extended Kalman filter stochastic perturbations Euler-Maruyama method |
| title | Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states |
| title_full | Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states |
| title_fullStr | Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states |
| title_full_unstemmed | Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states |
| title_short | Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states |
| title_sort | parameter estimation for networked sir models with stochastic perturbations using jekf a study using covid 19 daily data from indian states |
| topic | Graph Laplacian diffusion epidemic model joint extended Kalman filter stochastic perturbations Euler-Maruyama method |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2436662 |
| work_keys_str_mv | AT princeachankunju parameterestimationfornetworkedsirmodelswithstochasticperturbationsusingjekfastudyusingcovid19dailydatafromindianstates AT sarojkumardash parameterestimationfornetworkedsirmodelswithstochasticperturbationsusingjekfastudyusingcovid19dailydatafromindianstates |