ANALYZING COVID-19 DATA IN BALI USING DISCRETE TIME MARKOV CHAIN SIR MODEL - Roona Paranoan, Nicea
The Covid-19 pandemic, which began in December 2019, has hit many countries, including Indonesia. The spread pattern of an infectious disease could be modeled using a mathematical model, and one of these mathematical models is the Susceptible-Infected-Removed (SIR) model. A SIR model which follow a Markov process in discrete time is called the "Discrete Time Markov Chain SIR Model". As at 5 September 2020, Bali province is one of the provinces in Indonesia which has the highest positive cases of Covid-19 and is ranked first based on the high occupancy rate of Covid-19 at the referral hospitals. The research in this thesis tries to model the spread of Covid-19 in Bali province using the data recorded in the Indonesian “Covid-19 Handling Task Force” website, starting from the end of March 2020 to 7 September 2020. During the data cleaning and data validation process, it was found that some information was missing. Hence, what was originally planned, which was to analyze the data in a two-week period, could not be done. The author decided to use the data from the end of March 2020 to the end of June 2020, to “complete” the data from 30 June 2020 to 7 September 2020. Based on the “available data”, from 30 June 2020 to 7 September 2020, a Discrete Time Markov Chain SIR Model was applied to observe the spread of Covid-19 in Bali Province. The results of this study could be used as a preliminary analysis in observing the pattern of the spread of Covid-19 in Bali Province. The conclusions drawn are still preliminary in nature, without taking into account the "Indonesian Mortality Table"; the congenital disease of each individual; nor other characteristics of each individual.