Forecasting the spread of COVID-19 using supervised machine learning models

Abstract

Coronavirus disease of 2019 (COVID-19) has become widespread within few months and it has lead to a dramatic loss for human life worldwide. This pandemic impacts tens of millions of deaths each day and the number of people were dead by covid-19 is gradually increasing throughout the globe. During this pandemic situation control, we tend to propose a future prediction using Machine Learning algorithms on the death rate, the number of recovered estimates and the number of daily confirmed COVID-19 cases reported within the next ten days. It is based on Machine Learning technique. This forecasting method will predict the upcoming number of COVID-19 cases. Here we use four standard models for forecasting includes linear Regression (LR), The Lowest Absolute and Selective Shrinking Operator (LASSO), Vector Assistance (SVM) and exponential smoothing (ES) will predict the number of COVID-19 cases in future. These four models make three predictions: the mortality rates, the number of newly affected COVID-19 cases and the cummulative number of recovered cases in the next 10 days. These methods are better used in the COVID-19 situation. Based upon the findings, it is a encouraging method to use these standard models in the current situation of COVID-19 spread. The analysis shows that among all the standard forecasting models, ES model performs best, then LR and LASSO which also performs well in predicting the new infected cases of corona, death rate and recovery cases. Whereas the results of SVM were very bad in all the prediction scenarios from the given covid-19 data set. The predictions made by these models relating to the current situation are accurate and will also be useful for future awareness of the future situation. This paper will be enhanced continuously and next we are planning to traverse the prediction methodology using the updated covid-19 data set and we will make use of the most precise and best Machine Learning models for forecasting in future.