Hybridized wavelet neuronal learning-based modelling to predict novel COVID-19 effects in India and USA

Coronavirus disease so called as COVID-19 is an infectious disease and its spread takes place due to human interaction by their pathogen materials during coughing and sneezing. COVID-19 is basically a respiratory disease as evidence proved that a large number of infected people died due to short breathing. Most widely and uncontrollably spreading unknown viral genome infecting people worldwide was announced to be 2019-2020 nCoV by WHO on January 30, 2020. Based on the seriousness of its spread and unavailability of vaccination or any form of treatment, it was an immediate health-emergency of concern of international-level. The paper analyses effects of this virus in countries, such as India and United States on day-to-day basis because of their greater variability. In this study, various performance measures, such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination $$ , mean absolute standard error (MASE) and mean absolute percentage error (MAPE) which characterize models' performances. $$ value has been achieved to be closest to 1, i.e., 0.999 from Wavelet Neuronal Network Fuzzified Inferences' Layered Multivariate Adaptive Regression Spline (WNNFIL-MARS) for both the countries' data. It is important to capture the essence of this pandemic affecting millions of the population daily ever since its spread began from January, 2020.