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Submitted
Jun 28, 2023
Accepted
Jun 29, 2023
Published
Jun 30, 2023
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Abstract
The SIQS (Susceptible, Infective, Quarantine, and Susceptible) non-linear model is used to describe the dynamics of infectious diseases, especially optimizing individuals who are quarantined. Discretization of the SIQS model using the Runge-Kutta method and its physical interpretation is very useful if the model parameters can be estimated. Bayesian Markov Chain Monte Carlo for its numerical simulation. After 10,000 iterations, convergent and significant parameters were obtained, namely beta = 94.37, beta0 = -10.19, mu = -0.23 and b = 0.5.
Keywords
Bayesian
Disease
Markov Chain Monte Carlo
Runge-Kutta
SIQS
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
Usman, I. (2023). BAYESIAN MARKOV CHAIN MONTE CARLO SIMULATION OF NONLIENAR MODEL FOR INFECTIOUS DISEASES WITH QUARANTINE . Parameter: Journal of Statistics, 3(1), 46-53. https://doi.org/10.22487/27765660.2023.v3.i1.16445
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