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 daily active cases addition in Indonesia which is still changing dynamically. An alternative solution that can help to analyze countermeasures for the virus spread is modeling and simulating the spread of cases to estimate 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 modeling and utilizing the concept of optimization technology, the modeling process can be carried out more efficiently and accurately. A model is developed, one of the derivatives of SIR modeling, namely SIR-FV, based on the concept of optimization to estimate and simulate various virus spread scenarios. There are 2 scenarios developed for analysis, namely the vaccination program scenario and the contact rate scenario. 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. The contact rate scenario also has a significant impact. However, the simulation also shows that if the vaccination program is not supported by adequate health protocols, it will not have any impact on the prevention effort. These results apply to the results of the SIR-FV model. Overall, it can be concluded that the developed model can carry out all of its functions as needed, with the level of accuracy through the MAPE metric reaching 0.012 for the SIRFV model. © 2022 IEEE.
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. © 2022 IEEE.
ABSTRACT
Until now, it is not known when the COVID-19 pandemic in Indonesia will end. As COVID-19 cases continue to increase, predicting the number of cases infected with COVID-19 is very important to design a control strategy to reduce the disease spread. Towards the end of 2020, several manufacturers announced high efficacy rates of COVID-19 vaccine candidates. Vaccines have been believed to be an important tool for improving the health of the population so that the disease spread can be controlled without hindering economic growth. A Mathematical model of infectious diseases is an important tool that has been focused on predicting the dynamics of the disease spread. It can be used to predict the future situation of a potential outbreak and evaluate the best strategy to reduce the spread of the outbreak. There are many types of mathematical models to predict the behavior of an infectious disease that is transmitted from human to human. One of the commonly used is called the compartment model. In this paper, we use a modified SIR model with vaccination to predict the behavior of the disease spread after vaccination. Theoretically, a successful vaccination program should slow down the rate of the virus spread. The modified SIR model with vaccination is adopted to predict the spread of coronavirus. Here, we proof that the model has a unique equilibrium point that is globally asymptotically stable by using Lyapunov function if the vaccination rate is greater than zero. Otherwise, if there is no vaccination is done, the equilibrium points only stable if reproduction number of infection is less than one. Further, the model will be implemented to Indonesia data to predict the behavior of the spread of the disease after the vaccination program. © 2022 IEEE.