Big data analytics on the impact of OMICRON and its influence on unvaccinated community through advanced machine learning concepts

New SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus Type-2) variant termed to be “B.1.1.529” subtype mutation, which is a primary concern, might heavily influence further transmission, virulence and even affect the functioning of test methods and efficacy medications (vaccines). It is still not clear on the timeline for the Omicron (B.1.1.529) subtype to develop protective immunity or even when normal activities will rebound in our everyday lives. Computational analysis on the available big dataset of the Omicron variants’ and their effects on the unvaccinated population indicate that the concerned variant seemed to have a stronger propensity for the vulnerable group (unvaccinated community). In consequence of the terrible COVID-19 epidemic, scientific research on vaccine development and their future enhancement throughout the world have been stepped up significantly. We assessed approved vaccines’ effect on morbidity, hospital stays, and fatalities worldwide. Through available big datasets, an Ensemble learning strategy was used to estimate the likelihood of an unvaccinated person contracting a virus. Overall incidence rates dropped from 18.56 per cent to 2.8 per cent for the vaccinated community during the observation period. People ≥ 60 years had the most remarkable percentage drop (~ 15 per cent). In addition, about 70.4 per cent, immunization through vaccines lowered the number of hospitalizations (both ICU and non-ICUs) and fatalities. Through our research observations, the facts clear that immunization through vaccination has a significant influence on decreasing COVID-19 rapid transmission, even if it provides only a modest level of protection. However, to accomplish this effect, non-pharmaceutical therapies must be maintained indefinitely.