ABSTRACT
In this paper, the spread of the Covid-19 in.Indonesia is described by the SIRD epidemiological mathematical model. The mathematical model used in this paper is Susceptible, Infected, Recovered, Death (SIRD). The modified extended Kalman filter algorithm is applied to predict the spread of Covid-19 in.the future. We modified the algorithm by generating real data based on the previous estimation results. The real data generated from the generation is used at the correction stage to obtain prediction results in a fairly long period. Simulations were carried out with three types of mobility restrictions, namely mobility 100%, mobility 75%, and mobility 50%. Based on the simulation results, it can be concluded that mobility restrictions in Indonesia, which starts on September 4, 2020, can reduce the number of infected and death individuals and can increase the number of individuals who recover from Covid-19. © 2022 Author(s).
ABSTRACT
This paper aims to forecast and analyze the spread of COVID-19 outbreak in Indonesia by applying machine learning and hybrid approaches. We show the performance of each method, an ensemble-support vector regression (ensemble-SVR), a genetic algorithm and an SIRD model (GA-SIRD) and an extended Kalman filter, a genetic algorithm and an extended Kalman filter (EKF-GA-SIRD), in obtaining the prediction of the outbreak. The GA-SIRD model is built based on the data availability and is enhanced by employing an extended Kalman filter to better predict the spread of the outbreak. Without considering the epidemic model, the ensemble SVR can provide a higher accuracy compare to the two hybrid approaches in the case of short-term forecasting. Furthermore, the EKF-GA-SIRD can better adapt to the extreme change and shows a better performance than the GA-SIRD. © 2021.