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...
| Main Authors: | , , |
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| Format: | Book Chapter |
| Language: | English English |
| Published: |
Penerbit UMP
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37683/ 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 |
| Summary: | 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. |
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