Forecast and prediction of COVID-19 using machine learning

COVID-19 outbreaks only affect the lives of people, they result in a negative impact on the economy of the country. On Jan. 30, 2020, it was declared as a health emergency for the entire globe by the World Health Organization (WHO). By Apr. 28, 2020, more than 3 million people were infected by this virus and there was no vaccine to prevent. The WHO released certain guidelines for safety, but they were only precautionary measures. The use of information technology with a focus on fields such as data Science and machine learning can help in the fight against this pandemic. It is important to have early warning methods through which one can forecast how much the disease will affect society, on the basis of which the government can take necessary actions without affecting its economy. In this chapter, we include methods for forecasting future cases based on existing data. Machine learning approaches are used and two solutions, one for predicting the chance of being infected and other for forecasting the number of positive cases, are discussed. A trial was done for different algorithms, and the algorithm that gave results with the best accuracy are covered in the chapter. The chapter discusses autoregressive integrated moving average time series for forecasting confirmed cases for various states of India. Two classifiers, random forest and extra tree classifiers, were selected; both have an accuracy of more than 90%. Of the two, the extra tree classifier has 93.62% accuracy. These results can be used to take corrective measures by different governmental bodies. The availability of techniques for forecasting infectious disease can make it easier to fight COVID-19.