MODELING THE DYNAMICS OF COVID-19 CASES WITH DIFFERENCE EQUATION ON THE PSBB SCENARIO IN DKI JAKARTA

• Main Author: Kunti Nabila, Naradita
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/55954
• Institution: Institut Teknologi Bandung

The Covid-19 pandemic in Indonesia, especially DKI Jakarta, which has been going on since March 2020, is still showing no signs of ending soon. Various efforts have been made by the government, especially the DKI Jakarta Government in dealing with the dynamics of the Covid-19 case in DKI Jakarta. Starting from the implementation of the PSBB, PPKM, to the distribution of vaccinations to the community, which always acquire the pros and cons, especially at the beginning of the implementation because of the various assumptions and information in the society making the truth has not been proven. This study seeks to provide an overview of the dynamics of the Covid-19 case in DKI Jakarta and its relationship with the interventions provided by the government, the level of society compliance, and the readiness of public health services in dealing with Covid-19 patients based on data from trusted sources. This research was conducted by making a modeling based on difference equation orde 40 which is nonlinear, timevarying with positive feedback. This model utilizes 30 parameters, such as carrying capacity, alpha, transmission rate, patient resistance level at each stage of infection, duration in each stage of infection, detection coverage, etc., so it is flexible enough to adjust the value of each parameter according to actual conditions. In addition, this model is also able to accommodate the available vaccination dataset with the addition of vaccine efficacy level parameters. This study also simulates scenarios of health protocols that remain strict and relaxed regarding the current application of vaccines. It is concluded that the implementation of vaccination is useless if it is not accompanied by strict health protocols. Then, the search for optimal parameters for the development of this model uses the ParameterGrid which accommodates the train model with four parameters selected for optimization. This study resulted in an epidemiological model whose accuracy with the MAPE metric measure was 20.81% for daily cases and 3.75% for accumulative cases.