Coronavirus genomic infection-2019 (COVID-19) has been announced as a serious health emergency arising international awareness due to its spread to 201 countries at present. In the month of April of the year 2020, it has certainly taken the pandemic outbreak of approximately 11,16,643 infections confirmed leading to around 59,170 deaths have been recorded world-over. This article studies multiple countries-based pandemic spread for the development of the COVID-19 originated in the China. This paper focuses on forecasting via real-time responses data to inherit an idea about the increase and maximum number of virus-infected cases for the various regions. In addition, it will help to understand the panic that surrounds this nCoV-19 for some intensely affecting states possessing different important demographic characteristics that would be affecting the disease characteristics. This study aims at developing soft-computing hybrid models for calculating the transmissibility of this genome viral. The analysis aids the study of the outbreak of this virus towards the other parts of the continent and the world. A hybrid of wavelet decomposed data into approximations and details then trained & tested through neuronal-fuzzification approach. Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. While data-based prediction through interpolation applied through moving average predicts for longer time spans such as 50–60 days ahead with lesser accuracy as compared to that of wavelet-based hybrids. Based on the simulations, the significance level (alpha) ranges from 0.10 to 0.67, MASE varying from 0.06 to 5.76, sMAPE ranges from 0.15 to 1.97, MAE varies from 22.59 to 6024.76, RMSE shows a variation from 3.18 to 8360.29 & R 2 varying through 0.0018 to 0.7149. MASE and sMAPE are relatively lesser applied and novel measures that aimed to achieve increase in accuracy. They eliminated skewness and made the model outlier-free. Estimates of the awaited outburst for regions in this study are India, China and the USA that will help in the improvement of apportionment of healthcare facilities as it can act as an early-warning system for government policy-makers. Thus, data-driven analysis will provide deep insights into the study of transmission of this viral genome estimation towards immensely affected countries. Also, the study with the help of transmission concern aims to eradicate the panic and stigma that has spread like wildfire and has become a significant part of this pandemic in these times.

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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.

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year- 2012 MERS plus, most recently, 2019- nCoV eruptions. SARS-CoV & MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people. Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386 th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected ( y-z) , as exposed ones ( y ) change from 0 to 100 the value of asymptomatically infected ( z ) increased upto around 58, at exposed ones ( y ) =100, asymptomatically infected ( z ) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones ( y ) changes values upto 180, the value of asymptomatically infected ( z ) decreases to 25 so for exposed ones ( y ) from 100 to 180, asymptomatically infected ( z ) varies from 58 to 25 to 10 follows bifurcation. Also, phase structure...