High-Performing Machine Learning Algorithms for Predicting the Spread of COVID-19

COVID-19 is a strain of coronavirus that first broke out in Wuhan, China, in December 2019 and has since become a global pandemic. In this chapter, we apply four machine learning techniques, i.e., logistic regression (LR), support vector machine (SVM), recurrent neural network (RNN), and long short-time memory (LSTM) in predicting transmission of coronavirus (COVID-19). Data were collected from patients who have contacted coronavirus disease from 22 January 2020 to 14 March 2020 obtained from Kaggle database. The data consisted of the confirmed, death, and recovered cases of all the countries infected with coronavirus (COVID-19). The performance of each machine learning techniques was compared using mean absolute error (MAE), root mean square error (RMSE), and mean absolute scaled error (MASE). The results indicate that logistic regression (LR) is effective in predicting accurately different continents such as Africa, Asia, Australia/Oceania, Europe, North America, and South America and cruise ships with 0.4590, 57005.25, 0.6829, 44.35, 2.2764, 0.5401, and 4.7508, respectively. This is an indication that it is a promising technique in predicting the spread of coronavirus. The study reveals that there are upward trends of COVID-19 in Africa, Europe, Australia/Oceania, North America, and South America, while trends of transmission of pandemic diseases have been stable in Asia and Diamond Princess cruise ship. As COVID-19 cases continue to rise in Africa, Europe, Australia/Oceania, North America, and South America, there are urgent needs to curtail transmission of this disease.