COVID-19 pandemic in India: Forecasting using machine learning techniques

Forecasting about the Novel coronavirus disease 2019 (COVID-19) pandemic involves high uncertainty and may be affected by measures taken by the government to fight the disease. This research explores machine learning (ML) techniques to forecast the epidemiological trend of COVID-19 in India. We used 22 ML algorithms develop forecasting models and selected the four best ones on the basis of their performance using mean absolute percentage error (MAPE). Feature extraction and feature selection techniques were also employed to improve performance with cumulative and daily data obtained from Mar. 2 to Apr. 25, 2020. Because of the linear nature of cumulative data, the model built with these time series data outperforms with an MAPE of 0.498, 0.240, and 0.430, respectively, for cases that are confirmed or recovered and deaths using the extra tree regressor compared with the model built with daily data with an MAPE of 1.377, 1.302, and 0.488, respectively. Moreover, the study confirms that the models perform well at the validation stage with an MAPE of 4.123, 5.411, and 4.553, respectively, for confirmed or recovered cases and deaths using a model built with cumulative data and an MAPE of 6.261, 7.576, and 6.273, respectively, using a model built with daily data. On the basis of selected models, a 15-day forecast for confirmed and recovered cases and deaths from COVID-19 was performed that can be validated in the near future. However, it depends on precaution measures taken by the central and state governments as well as individuals, including social distancing, self-isolation from society, restrictions in bus, rail, and air transport, school, college, and market closings or openings, the extension of the lockdown period, privileges to be given during lockdown, and other measures, as well whether guidelines issued by government from time to time were followed.