Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading
COVID-19 pandemic was identified in Wuhan, China in 2019 and has spread at a tremendous rate affecting all countries over the world. Understanding the spreading disease is crucial; hence, the dynamic behaviour of the disease can be predicted. This paper is aimed to model the COVI...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book Chapter |
| Language: | en en |
| Published: |
Penerbit UMP
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37683/1/Predictive%20Analytics%20of%20the%20Covid-19%20Outbreak%20Under%20Uncertainty%20of%20the%20Disease%20Spreading.pdf http://umpir.ump.edu.my/id/eprint/37683/2/%28FULL%29%20PREDICTIVE%20ANALYTICS%20OF%20THE%20COVID-19%20OUTBREAK%20UNDER%20UNCERTAINTY%20OF%20THE%20DISEASE%20SPREADING.pdf http://umpir.ump.edu.my/id/eprint/37683/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1831529411514466304 |
|---|---|
| author | Norhayati, Rosli Noryanti, Muhammad Muhammad Fahmi, Ahmad Zuber |
| author_facet | Norhayati, Rosli Noryanti, Muhammad Muhammad Fahmi, Ahmad Zuber |
| author_sort | Norhayati, Rosli |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | COVID-19 pandemic was identified in Wuhan, China in 2019 and has spread at a tremendous rate affecting all countries over the world. Understanding the spreading disease is crucial; hence, the dynamic behaviour of the disease can be predicted. This paper is aimed to model the COVID-19 outbreak by extending the deterministic susceptible-infected-recovered-death (DSIRD) into a stochastic SIRD (SSIRD) model. Infectious rate parameter of the DSIRD model is perturbed with Brownian motion to reflect the uncertainty of the COVID-19 outbreak. Fourth order stochastic Runge-Kutta (SRK4) method is used to simulate the model. Parameter estimation is estimated using the Markov Chain Monte Carlo (MCMC) method. The simulated results for three ASEAN countries of Malaysia, Indonesia and Singapore indicate that SSIRD model is consistent with the infected COVID-19 data;hence, shows the model is adequate in explaining the behaviour of the infectious disease. |
| format | Book Chapter |
| id | my.ump.umpir.37683 |
| institution | Universiti Malaysia Pahang |
| language | en en |
| publishDate | 2023 |
| publisher | Penerbit UMP |
| record_format | eprints |
| spelling | my.ump.umpir.376832023-10-27T04:17:22Z http://umpir.ump.edu.my/id/eprint/37683/ Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading Norhayati, Rosli Noryanti, Muhammad Muhammad Fahmi, Ahmad Zuber QA Mathematics COVID-19 pandemic was identified in Wuhan, China in 2019 and has spread at a tremendous rate affecting all countries over the world. Understanding the spreading disease is crucial; hence, the dynamic behaviour of the disease can be predicted. This paper is aimed to model the COVID-19 outbreak by extending the deterministic susceptible-infected-recovered-death (DSIRD) into a stochastic SIRD (SSIRD) model. Infectious rate parameter of the DSIRD model is perturbed with Brownian motion to reflect the uncertainty of the COVID-19 outbreak. Fourth order stochastic Runge-Kutta (SRK4) method is used to simulate the model. Parameter estimation is estimated using the Markov Chain Monte Carlo (MCMC) method. The simulated results for three ASEAN countries of Malaysia, Indonesia and Singapore indicate that SSIRD model is consistent with the infected COVID-19 data;hence, shows the model is adequate in explaining the behaviour of the infectious disease. Penerbit UMP 2023-04-10 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37683/1/Predictive%20Analytics%20of%20the%20Covid-19%20Outbreak%20Under%20Uncertainty%20of%20the%20Disease%20Spreading.pdf pdf en http://umpir.ump.edu.my/id/eprint/37683/2/%28FULL%29%20PREDICTIVE%20ANALYTICS%20OF%20THE%20COVID-19%20OUTBREAK%20UNDER%20UNCERTAINTY%20OF%20THE%20DISEASE%20SPREADING.pdf Norhayati, Rosli and Noryanti, Muhammad and Muhammad Fahmi, Ahmad Zuber (2023) Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading. In: Emerging Technologies During the Era of Pandemic COVID-19. Penerbit UMP, UMP, pp. 43-56. ISBN 978-967-2831-77-8 |
| spellingShingle | QA Mathematics Norhayati, Rosli Noryanti, Muhammad Muhammad Fahmi, Ahmad Zuber Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading |
| title | Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading |
| title_full | Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading |
| title_fullStr | Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading |
| title_full_unstemmed | Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading |
| title_short | Predictive Analytics of the Covid-19 Outbreak Under Uncertainty of the Disease Spreading |
| title_sort | predictive analytics of the covid-19 outbreak under uncertainty of the disease spreading |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/37683/1/Predictive%20Analytics%20of%20the%20Covid-19%20Outbreak%20Under%20Uncertainty%20of%20the%20Disease%20Spreading.pdf http://umpir.ump.edu.my/id/eprint/37683/2/%28FULL%29%20PREDICTIVE%20ANALYTICS%20OF%20THE%20COVID-19%20OUTBREAK%20UNDER%20UNCERTAINTY%20OF%20THE%20DISEASE%20SPREADING.pdf http://umpir.ump.edu.my/id/eprint/37683/ |
| url_provider | http://umpir.ump.edu.my/ |