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Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia

This paper proposes a compartmental Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model for COVID-19 cases in Malaysia. This extended model is more relevant to describe the disease transmission than the SIRD model since the exposed (E) compartment represents individuals in the disease's...

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Main Authors: Zulkarnain, Norsyahidah, Abdul Hadi, Muhammad Salihi, Mohammad, Nurul Farahain, Shogar, Ibrahim
Format: Article
Language:English
Published: Penerbit UTM Press 2023
Subjects:
QA155 Algebra
Online Access:http://irep.iium.edu.my/105021/7/105021_Fitting%20time-varying%20coefficients%20SEIRD%20model.pdf
http://irep.iium.edu.my/105021/
https://ijic.utm.my/index.php/ijic/article/view/397/257
https://doi.org/10.11113/ijic.v13n1.397
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spelling my.iium.irep.1050212023-06-10T02:05:58Z http://irep.iium.edu.my/105021/ Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia Zulkarnain, Norsyahidah Abdul Hadi, Muhammad Salihi Mohammad, Nurul Farahain Shogar, Ibrahim QA155 Algebra This paper proposes a compartmental Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model for COVID-19 cases in Malaysia. This extended model is more relevant to describe the disease transmission than the SIRD model since the exposed (E) compartment represents individuals in the disease's incubation period. The mathematical model is a system of ordinary differential equations (ODEs) with time-varying coefficients as opposed to the conventional model with constant coefficients. This time dependency treatment is necessary as the epidemiological parameters such as infection rate β, recovery rate γ, and death rate μ usually change over time. However, this feature leads to an increasing number of unknowns needed to be solved to fit the model with the actual data. Several optimization algorithms under Python’s LMfit package, such as Levenberg-Marquardt, Nelder-Mead, Trust-Region Reflective and Sequential Linear Squares Programming; are employed to estimate the related parameters, in such that the numerical solution of the ODEs will fit the data with the slightest error. Nelder-Mead outperforms the other optimization algorithm with the least error. Qualitatively, the result shows that the proportion of the quarantine rule-abiding population should be maintained up to 90% to ensure Malaysia successfully reaches disease-free or endemic equilibrium. Penerbit UTM Press 2023-05-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/105021/7/105021_Fitting%20time-varying%20coefficients%20SEIRD%20model.pdf Zulkarnain, Norsyahidah and Abdul Hadi, Muhammad Salihi and Mohammad, Nurul Farahain and Shogar, Ibrahim (2023) Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia. International Journal of Innovative Computing, 13 (1). pp. 59-68. E-ISSN 2180-4370 https://ijic.utm.my/index.php/ijic/article/view/397/257 https://doi.org/10.11113/ijic.v13n1.397
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA155 Algebra
spellingShingle QA155 Algebra
Zulkarnain, Norsyahidah
Abdul Hadi, Muhammad Salihi
Mohammad, Nurul Farahain
Shogar, Ibrahim
Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia
description This paper proposes a compartmental Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model for COVID-19 cases in Malaysia. This extended model is more relevant to describe the disease transmission than the SIRD model since the exposed (E) compartment represents individuals in the disease's incubation period. The mathematical model is a system of ordinary differential equations (ODEs) with time-varying coefficients as opposed to the conventional model with constant coefficients. This time dependency treatment is necessary as the epidemiological parameters such as infection rate β, recovery rate γ, and death rate μ usually change over time. However, this feature leads to an increasing number of unknowns needed to be solved to fit the model with the actual data. Several optimization algorithms under Python’s LMfit package, such as Levenberg-Marquardt, Nelder-Mead, Trust-Region Reflective and Sequential Linear Squares Programming; are employed to estimate the related parameters, in such that the numerical solution of the ODEs will fit the data with the slightest error. Nelder-Mead outperforms the other optimization algorithm with the least error. Qualitatively, the result shows that the proportion of the quarantine rule-abiding population should be maintained up to 90% to ensure Malaysia successfully reaches disease-free or endemic equilibrium.
format Article
author Zulkarnain, Norsyahidah
Abdul Hadi, Muhammad Salihi
Mohammad, Nurul Farahain
Shogar, Ibrahim
author_facet Zulkarnain, Norsyahidah
Abdul Hadi, Muhammad Salihi
Mohammad, Nurul Farahain
Shogar, Ibrahim
author_sort Zulkarnain, Norsyahidah
title Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia
title_short Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia
title_full Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia
title_fullStr Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia
title_full_unstemmed Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia
title_sort fitting time-varying coefficients seird model to covid-19 cases in malaysia
publisher Penerbit UTM Press
publishDate 2023
url http://irep.iium.edu.my/105021/7/105021_Fitting%20time-varying%20coefficients%20SEIRD%20model.pdf
http://irep.iium.edu.my/105021/
https://ijic.utm.my/index.php/ijic/article/view/397/257
https://doi.org/10.11113/ijic.v13n1.397
_version_ 1769841824042582016
score 13.211869

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