Dynamics of COVID-19 in India using WANFIS (Wavelet Adaptive Neuro-Fuzzy Inference System) model

The preset paper discusses the COVID-19 pandemic in India and the development of a data-driven model to predict COVID-19 confirmed cases, casualties, and recoveries in the country. The Coronavirus Disease 2019 (COVID-19) was first identified in December 2019 in the Hubei Province of the People's Republic of China. It quickly spread to 220 countries worldwide and had a significant impact. In India, the second wave of COVID-19 hit in April 2021, resulting in over 40 million reported cases and three lakh casualties. India ranked second in COVID-19 infections globally, after the United States of America. To better understand the dynamics of the COVID-19 pandemic in India, a data-driven WANFIS (Wavelet Adaptive Neuro-Fuzzy Inference System) model was developed. This model uses discrete wavelet decomposition to extract information from input data and predict the escalation of confirmed cases, casualties, and recoveries in India. The WANFIS model's effectiveness was compared to other models like the artificial neural network (ANN) model and individual ANFIS model, and it proved to be more robust in predicting COVID-19 transmission. The proposed WANFIS model has the potential to effectively forecast the transmission of infectious diseases, enabling government and health officials to anticipate and prepare for emergencies more effectively.