TIME-SERIES MODELING OF COVID-19 USING MACHINE LEARNING TECHNIQUES
Abstract
The COVID-19 pandemic was first detected in China in late 2019 and then spread around the world. As of July 31st, 2019, more than 17 million cases have been infected, including more than 670,000 deaths worldwide. In the current study, two machine learning models are Nonlinear autoregressive (NAR) and Long short-term memory (LSTM), were developed to forecast outbreaks of COVID-19 globally and some of the most heavily affected countries such as the United State, Brazil, and India based on a public data set provided by the World Health Organization (WHO). A set of metrics evaluated the performance of the forecasting models as the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and coefficient of determination (R2) is given to analyze and evaluate the accuracy of the forecasting models. The results showed that both models are suitable for forecasting the spread of COVID-19, and NAR gave a higher performance than LSTM. The forecasting results can be used for policy formulation and precautions in the studied countries.
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