Aplikasi Persamaan Diferensial Stokastik Pada Masalah Kontrol Optimum Berkendala

Pasaribu, Meliana; Helmi, Helmi

doi:10.30598/barekengvol15iss2pp257-266

Cited by 1 publication

(1 citation statement)

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“…During this period, there was no vaccine available, and immigration and emigration factors were not considered, irrespective of age, gender, and being closed in nature, referring to a situation where the natural death rate and natural birth rate are the same. The parameters used were modified from previous research from Pasaribu and Helmi, et al, Manaqib, et al [9] with subpopulations including susceptible, infected, quarantined, and recovered subpopulations. The aim of this study is to determine the equilibrium points of the COVID-19 spread modeling and analyze stability as well as the significant impact of delay time in the modeling.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Spread of Covid-19 Disease With Time Delay in Pontianak City

Fatonah,

Noviani,

Pasaribu

2024

BAREKENG: J. Math. & App.

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Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a coronavirus originating from the city of Wuhan in 2019. This disease affects the respiratory system. The city of Pontianak has the highest population density in West Kalimantan. This density results in a higher spread of Covid-19. In this article, the spread of COVID-19 is formulated into a mathematical model, equilibrium points are sought, stability is analyzed, and a delay time is introduced to reduce the spread of COVID-19. The magnitude of the delay time given during quarantine complies with health protocols, which is between 2 – 14 days. This article aims to analyze the influence of the delay time in modeling the spread of Covid-19. The problem of COVID-19 spread is constructed into an SIQR model, with a sub-population of recovered individuals returning to the susceptible sub-population. The population is divided into four sub-populations: susceptible (S), Infected (I), Quarantined (Q), and Recovered (R). The parameters used include the natural birth rate ( ), the rate of susceptibility to infection ( ), the rate of infection under quarantine ( ), the recovery rate from infection ( ), the recovery rate from infection under quarantine ( ), the death rate from infection ( ), the death rate under quarantine ( ), the delay time from infection to quarantine process ( ), the natural death rate ( ), and the rate of recovered immunity returning to susceptibility ( ). The simulation results show that when the basic reproduction number is less than , the disease-free equilibrium is stable, and when the basic reproduction number is greater than , the endemic equilibrium point is stable. The addition of a time delay ( ) in the SIQR model affects the stability of the endemic equilibrium point but does not affect the stability of the disease-free equilibrium point.

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Section: Introductionmentioning

confidence: 99%