Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters , RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.