Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method

The susceptible-infectious-recover-death SIRD deterministic compartmental model is the most frequent mathematical model of the epidemic outbreak. The model consists of four states, susceptible, infected, recovered and death. Pandemic outbreak is highly influenced by the uncontrolled factors of the e...

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Main Authors: Muhammad Fahmi, Ahmad Zuber, Norhayati, Rosli, Noryanti, Muhammad
Format: Conference or Workshop Item
Language:English
English
Published: AIP Publishing 2023
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/42343/
http://umpir.ump.edu.my/id/eprint/42343/1/8.2_Parametrization%20of%20the%20stochastic%20SIRD%20model%20for%20COVID-19%20outbreak%20using%20Markov%20Chain%20Monte%20Carlo%20method.pdf
http://umpir.ump.edu.my/id/eprint/42343/7/Parametrization%20of%20the%20Stochastic%20SIRD%20Model%20for%20COVID-19%20Outbreak.pdf
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author Muhammad Fahmi, Ahmad Zuber
Norhayati, Rosli
Noryanti, Muhammad
author_facet Muhammad Fahmi, Ahmad Zuber
Norhayati, Rosli
Noryanti, Muhammad
author_sort Muhammad Fahmi, Ahmad Zuber
building UMP Institutional Repository
collection Online Access
description The susceptible-infectious-recover-death SIRD deterministic compartmental model is the most frequent mathematical model of the epidemic outbreak. The model consists of four states, susceptible, infected, recovered and death. Pandemic outbreak is highly influenced by the uncontrolled factors of the environmental noise. This paper is at aimed to extend the deterministic model of SIRD to a stochastic SIRD counterpart. The epidemiological parameters are perturbed with the noisy behavior of the Wiener process to gain insight of the noisy behaviour of the outbreak. The parameters representing the rate between the four states (infection rate, recovery rate, fatality rate and immune lost rate) are estimated using the Markov Chain Monte Carlo (MCMC) method using 200, 400 and 1000 simulations. The result shows that as the number of sample paths is increased (1000 simulations), the parameter estimated from the model provide low value of the Monte-Carlo error and root mean square error (RMSE), hence indicate 1000 simulation of the MCMC provide acceptable estimated value of the epidemiological parameter for model simulation.
first_indexed 2025-11-15T03:47:09Z
format Conference or Workshop Item
id ump-42343
institution Universiti Malaysia Pahang
institution_category Local University
language English
English
last_indexed 2025-11-15T03:47:09Z
publishDate 2023
publisher AIP Publishing
recordtype eprints
repository_type Digital Repository
spelling ump-423432024-08-14T02:14:33Z http://umpir.ump.edu.my/id/eprint/42343/ Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method Muhammad Fahmi, Ahmad Zuber Norhayati, Rosli Noryanti, Muhammad QA Mathematics The susceptible-infectious-recover-death SIRD deterministic compartmental model is the most frequent mathematical model of the epidemic outbreak. The model consists of four states, susceptible, infected, recovered and death. Pandemic outbreak is highly influenced by the uncontrolled factors of the environmental noise. This paper is at aimed to extend the deterministic model of SIRD to a stochastic SIRD counterpart. The epidemiological parameters are perturbed with the noisy behavior of the Wiener process to gain insight of the noisy behaviour of the outbreak. The parameters representing the rate between the four states (infection rate, recovery rate, fatality rate and immune lost rate) are estimated using the Markov Chain Monte Carlo (MCMC) method using 200, 400 and 1000 simulations. The result shows that as the number of sample paths is increased (1000 simulations), the parameter estimated from the model provide low value of the Monte-Carlo error and root mean square error (RMSE), hence indicate 1000 simulation of the MCMC provide acceptable estimated value of the epidemiological parameter for model simulation. AIP Publishing 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42343/1/8.2_Parametrization%20of%20the%20stochastic%20SIRD%20model%20for%20COVID-19%20outbreak%20using%20Markov%20Chain%20Monte%20Carlo%20method.pdf pdf en http://umpir.ump.edu.my/id/eprint/42343/7/Parametrization%20of%20the%20Stochastic%20SIRD%20Model%20for%20COVID-19%20Outbreak.pdf Muhammad Fahmi, Ahmad Zuber and Norhayati, Rosli and Noryanti, Muhammad (2023) Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method. In: AIP Conference Proceedings. 15th Universiti Malaysia Terengganu Annual Symposium 2021, UMTAS 2021 , 23 - 25 November 2021 , Virtual, Online. pp. 1-8., 2746 (1). ISSN 0094-243X ISBN 9780735446625 (Published) https://doi.org/10.1063/5.0152265
spellingShingle QA Mathematics
Muhammad Fahmi, Ahmad Zuber
Norhayati, Rosli
Noryanti, Muhammad
Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method
title Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method
title_full Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method
title_fullStr Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method
title_full_unstemmed Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method
title_short Parametrization of the stochastic SIRD model for covid-19 outbreak using Markov chain Monte Carlo method
title_sort parametrization of the stochastic sird model for covid-19 outbreak using markov chain monte carlo method
topic QA Mathematics
url http://umpir.ump.edu.my/id/eprint/42343/
http://umpir.ump.edu.my/id/eprint/42343/
http://umpir.ump.edu.my/id/eprint/42343/1/8.2_Parametrization%20of%20the%20stochastic%20SIRD%20model%20for%20COVID-19%20outbreak%20using%20Markov%20Chain%20Monte%20Carlo%20method.pdf
http://umpir.ump.edu.my/id/eprint/42343/7/Parametrization%20of%20the%20Stochastic%20SIRD%20Model%20for%20COVID-19%20Outbreak.pdf