Analysis and Simulation of the Impact of Vaccination on the Spread of COVID-19 in Indonesia Using SIR and SIR-F Modelling

Abstract

The COVID-19 virus pandemic in Indonesia has been going on since March 2020 and is still ongoing with conditions that need to be watched out for. This can be seen from the distribution of the addition of daily active cases in Indonesia which is still changing dynamically. An alternative solution that can help to analyze the prevention of the spread of the virus is modelling and simulating the spread of cases to estimate the description of pandemic conditions that may occur in Indonesia. A common and widely used epidemiological-based model is the SIR model, which groups individuals affected by a pandemic into several compartments. Using this modelling and utilizing the concept of machine learning technology, the modelling process can be carried out more efficiently and accurately. In this final project, two models are developed, namely SIR and one of its derivatives, SIR-F, based on machine learning concepts to estimate and simulate various scenarios of virus spread. There are 3 scenarios developed for analysis, namely the scenario without a vaccination program, a vaccination program with a health protocol that is adhered to, and a vaccination program that is not followed by a health protocol. Based on the scenario simulation, it was found that the vaccination program could have a positive impact on efforts to deal with the COVID-19 pandemic more effectively when compared to the scenario without vaccination. Meanwhile, if the vaccination program is not supported by adequate health protocols, then vaccination will not have any impact on the prevention effort. These results apply uniformly to the results of the SIR and SIR-F models. Overall, it can be concluded that the developed model can carry out all its functions as needed, with the level of accuracy through the MAPE metric reaching 0.412 for the SIR model and 0.022 for the SIR-F model.