ANALYSIS AND SIMULATION OF COVID-19 SPREAD IN INDONESIA USING SIR-QFV MODELLING WITH OPTIMIZATION

• Main Author: Hidajat, Christovito
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/66556
• Institution: Institut Teknologi Bandung

Indonesia is one of the countries that has been heavily affected by the COVID-19 virus pandemic. Since March 2020, Indonesia has experienced three waves of COVID-19, with the peak of active cases occurring around February 2021 for wave 1, July 2021 for wave 2, and February 2022 for wave 3. There are various efforts by the Indonesian government to suppress the spread of COVID-19 in Indonesia, including issuing health protocol policies and vaccination programs. However, this still raises concerns in the community regarding the risk of spreading COVID-19 due to various uncertainties. One alternative solution that can be done to understand the dynamics of the spread of COVID-19 is to model the endemic disease compartment. In this final project, the SIR-QFV model is developed which consists of susceptible, infected, recovered, quarantined, fatal, and vaccinated compartments. To create the SIR-QFV model, the distribution time was divided into phases based on the analysis of the S-R trend into 73 phases as of June 2, 2022. For each phase, the distribution parameter was estimated using Optuna. The optimized SIR-QFV model can be used to simulate several pandemic spread scenarios. The simulation was carried out with 2 scenarios, namely manipulation of contact rate and vaccination rate. As a result, it can be concluded that suppression of contact rates with health protocols can reduce cases and vice versa. Meanwhile, the vaccination process will have a significant impact on reducing cases if the health protocol is still implemented. Calculation of model performance with several evaluation metrics results in the conclusion that the SIR-QFV model can be used as an alternative in understanding the dynamics of the spread of COVID-19. The model has an R2 score of 0.866, RMSE of 2,434, RMSE of 0.026, MAE of 951, and MAPE of 0.012.