Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up

Abstract

COVID-19 has spread around the world since it begun in December 2019. The pandemic has created an unprecedented global health emergency since World War II. This paper studies the impact of pandemic and predicts the anticipated casualty rise in India. The data has been extracted from the API provided by https://www.covid19india.org/ and covers up the time period from 30th January 2020 when the first case occurred in India till 13th January 2021. The paper provides a comparative study of six machine learning algorithms namely SMOreg, Random Forest, lBk, Gaussian Process, Linear Regression, and Autoregressive Integrated Moving Average (ARIMA) in forecasting deceased COVID 19 cases, via the data mining tool such as Weka and R. The major findings show that the best predictor model for anticipating the frequency of deceased cases in India is ARIMA (5,2,0). Utilizing this model, we estimated the propagation rate of deceased cases for the next month. The findings reveal that the fatal cases in India could rise from 151,174 to 157,179 within one month with an average of 190 death reports every day. This study will be helpful for the Indian Government and Medical Practitioners in assessing the spread of pandemic in India and devising a combat plan to mitigate the pandemic.