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Journal : Proceeding of International Conference on Humanity Education and Society

SIMULATION OF DISCRETE-TIME MARKOV CHAIN SUSCEPTIBLE VACCINATED INFECTED RECOVERED SUSCEPTIBLE (DTMC SVIRS) STOCHASTIC EPIDEMIC MODEL ON THE SPREAD OF TUBERCULOSIS DISEASE IN CENTRAL JAVA Arnandya, Evelyn Regita; Respatiwulan, Respatiwulan; Susanti, Yuliana
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

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Abstract

One of the infectious diseases that is still a public health challenge in Indonesia is tuberculosis (TB). This study is intended to model the spread of TB disease in Central Java using the Discrete-Time Markov Chain Susceptible Vaccinated Infected Recovered Susceptible (DTMC SVIRS) stochastic epidemic model. This model categorizes the population into four groups: susceptible, vaccinated, infected, and recovered. The transition probabilities between these groups are obtained based on transmission, vaccination, vaccine failure, vaccine effectiveness, recovery, and waning immunity rates. Parameter values were estimated using TB data from the Central Java Health Profile. Simulations were performed with different transmission rate treatments to analyze their effect on epidemic dynamics. The results show that the higher transmission rate, the longer it takes to reach the peak of epidemic and the more individuals are infected, which indicates a more serious epidemic. The model predicts that the epidemic will continue timelessly due to waning immunity and remaining susceptibility. The SVIRS model provides an overview of the spread of TB in Central Java.
SIMULATION OF DISCRETE-TIME MARKOV CHAIN SUSCEPTIBLE VACCINATED INFECTED RECOVERED SUSCEPTIBLE (DTMC SVIRS) STOCHASTIC EPIDEMIC MODEL ON THE SPREAD OF TUBERCULOSIS DISEASE IN CENTRAL JAVA Arnandya, Evelyn Regita; Respatiwulan, Respatiwulan; Susanti, and Yuliana
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the infectious diseases that is still a public health challenge in Indonesia is tuberculosis (TB). This study is intended to model the spread of TB disease in Central Java using the Discrete-Time Markov Chain Susceptible Vaccinated Infected Recovered Susceptible (DTMC SVIRS) stochastic epidemic model. This model categorizes the population into four groups: susceptible, vaccinated, infected, and recovered. The transition probabilities between these groups are obtained based on transmission, vaccination, vaccine failure, vaccine effectiveness, recovery, and waning immunity rates. Parameter values were estimated using TB data from the Central Java Health Profile. Simulations were performed with different transmission rate treatments to analyze their effect on epidemic dynamics. The results show that the higher transmission rate, the longer it takes to reach the peak of epidemic and the more individuals are infected, which indicates a more serious epidemic. The model predicts that the epidemic will continue timelessly due to waning immunity and remaining susceptibility. The SVIRS model provides an overview of the spread of TB in Central Java.
SIMULATION OF THE DISCRETE TIME MARKOV CHAIN SUSCEPTIBLE INFECTED RECOVERED (DTMC SIR) EPIDEMIC MODEL FOR COVID-19 TRANSMISSION IN CENTRAL JAVA Valentino, Yohanes Felix; Respatiwulan, Respatiwulan; Slamet, Isnandar
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

An epidemic is a situation when an area has a very high number of cases of individuals infected with an infectious disease in a short time frame. Susceptible Infected Recovered (SIR) epidemic models that explain changes in the number of infected individuals in discrete time intervals are called Discrete Time Markov Chain SIR (DTMC SIR) epidemic models. This research aims to discuss the DTMC SIR epidemic model and its simulation of the COVID-19 outbreak. The research methods used are literature reviews and simulation of the dynamics of COVID-19 transmission in Central Java. Central Java's COVID-19 dynamics are analyzed using the obtained DTMC SIR model with a contact rate and cure rate . This research has yielded a DTMC SIR epidemic model that uses transition probabilities to study the dynamics of COVID-19 transmission. The model applied with an initial value of and , and shows that COVID-19 stops when and occurs at . The model was also applied when the contact rate was reduced and increased. The conclusion is that the smaller the contact rate, the longer the epidemic ends and the fewer individuals are infected at the time the epidemic ends.