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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021 ; : 83-88, 2021.
Article in English | Scopus | ID: covidwho-1708987


Infectious diseases can have an enormous impact on the public because they negatively affect not only mortality but also unemployment and other social impacts. It is crucial to anticipate additional resources to counter infectious diseases mathematical and statistical tools that can be used to generate forecasts of reported cases. In this paper, the multivariable autoregression methods were compared for forecasting infectious diseases. We discuss the methods and use them to forecast infectious diseases. In this case, we used several COVID-19 cases as the object of forecasting. We used three prediction methods as Vector Autoregression (VAR), Vector Autoregression Moving Average (VARMA), and Autoregression Moving Average with exogenous variable (VARMA-X). The results show that the models have different results, among three methods, VAR give the best result of forecasting daily covid case for both stationary and non-stationary data. While VARMA-X shows the lowest performance for forecasting the dataset. We suggest by combining the AR model with the ANN model can provide a better result for forecasting. © 2021 IEEE.