Applying Machine Learning Prediction Methods to COVID-19 Data

Abstract

The Coronavirus (COVID-19) epidemic emerged in China and has caused many problems such as loss of life, and deterioration of social and economic structure. Thus, understanding and predicting the course of the epidemic is very important. In this study, SEIR model and machine learning methods LSTM and SVM were used to predict the values of Susceptible, Exposed, Infected, and Recovered for COVID-19. For this purpose, COVID-19 data of Egypt and South Korea provided by John Hopkins University were used. The results of the methods were compared by using MAPE. Total 79% of MAPE were between 0-10. The comparisons show that although LSTM provided the better results, the results of all three methods were successful in predicting the number of cases, the number of patients who died, the peaks and dimensions of the epidemic.

Keywords

• Machine learning
• SEIR
• BFGS
• LSTM
• SVM
• COVID-19

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