Real-time estimation of COVID-19 cases using machine learning and mathematical models - The case of India

Abstract

COVID-19 pandemic has stressed out the economy and resources of major countries across the world due to its high infection and transmission rate. The count of COVID-19 cases skyrocketed in the past few days, which creates immense pressure on health officials and governments. Therefore, prediction models to determine the number of new infections are urgently required in such grave times. In the present study, a machine learning technique, namely artificial neural network (ANN) is proposed to forecast the COVID-19 outbreak in India, for the first time. Moreover, in our study, we have additionally attempted to use a mathematical curve fitting model to ascertain the performance of the proposed ANN-based machine learning model. In addition, the impact of preventive measures such as lockdown and social distancing on the spread of COVID-19 is also analyzed by estimating the growth of the epidemic under different transmission rates. Moreover, a comparison between the proposed and existing COVID-19 prediction models is also demonstrated. Intriguingly, the proposed model is found to be highly accurate in estimating the growth of COVID-19 related parameters with the lowest MAPE values (cumulative confirmed cases (3.981), daily confirmed cases (4.173) and cumulative deceased cases (4.413)). Hence, the present study can assist the health officers and administration in getting prepared with the beforehand arrangement of the required resources and medical facilities.