MATHEMATICAL MODELLING OF POSTPONEMENT OF DOSE 2 VACCINATION EFFECT ON THE SPREAD OF COVID-19 IN INDONESIA

The whole world is in chaos due to the COVID-19 pandemic. Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2, a type of coronavirus. It is feared that the very high spread rate could lead to an increase in infected individuals and an even higher death rate due to CO...

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Bibliographic Details
Main Author: Vasya Pratiwi, Nadira
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/65421
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:The whole world is in chaos due to the COVID-19 pandemic. Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2, a type of coronavirus. It is feared that the very high spread rate could lead to an increase in infected individuals and an even higher death rate due to COVID-19. Therefore, the Indonesian government focus in dealing with the COVID-19 virus with a vaccination program for every community. This study will model the effect of poseponement dose 2 vaccination against spread of COVID-19 in Indonesia by modifying the SIRV compartment model and analyzed the mathematical model. From this model, a numerical simulation of the dynamics of the spread of COVID- 19 in Indonesia when disease-free and endemic. Several parameters will be used to determine the sensitivity against basic reproduction numbers, infection cases, and death cases. It was found that the more individuals who delay and the longer the individual delays the implementation of vaccine 2, the higher the value of the basic reproduction number. Then, it can also have an impact on cases of infection and cases of death due to COVID-19 which are getting higher. Parameter estimation and simulation were carried out based on the data, it was found that the longer delay of dose 2 vaccination and the smaller rate of dose I vaccination can cause the value of R0 to be higher. This is also corresponding to the increasing cases of infection.