COVID-19 Epidemic Analysis and Prediction Using Machine Learning Algorithms

Solanki, Arun; Singh, Tarana

COVID-19 is a real problem, and it is spreading like a forest fire. The data of this pandemic is time-series data. The models that can handle time-series data are the ARIMA model, the Holt-Winter model, the SARIMAX model, polynomial regression, and LSTM. These models have been applied to COVID-19 data, and the results are discussed with significance. This chapter used three types of datasets. The primary dataset is the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE (https://github.com/CSSEGISandData/COVID-19). The second dataset is used from Worldometers website (https://www.worldometers.info/), and third is from Kaggle. The SARIMAX model produced 0.236 as the MAPE value, while the Holt-Winter model produced 0.249. The polynomial regression model shows that the accuracy of the model approximated for the tenth day is 85% in the prediction of the number of affected cases and the number of deaths. The LSTM model used the ADAM optimizer and calculated the root mean square error. The prediction error for training is 6.45, and the calculated overall error is 5.34.