ABSTRACT
This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.
ABSTRACT
ObjectiveTo investigate the abrogation of COVID-19 case declines from predicted rates in the US in relationship to viral variants and mutations. DesignEpidemiological prediction and time series study of COVID-19 in the US by State. SettingCommunity testing and sequencing of COVID-19 in the US. ParticipantsTime series US COVID-19 case data from the Johns Hopkins University CSSE database. Time series US Variant and Mutation data from the GISAID database. Main outcome measuresPrimary outcomes were statistical modeling of US state deviations from epidemiological predictions, percentage of COVID-19 variants, percentage of COVID-19 mutations, and reported SARS-CoV-2 infections. ResultsDeviations in epidemiological predictions of COVID-19 case declines in the North Eastern US in March 2021 were highly positively related to percentage of B.1.526 (Iota) lineage (p < 10e - 7) and B.1.526.2 (p < 10 - 8) and the T95I mutation (p < 10e - 9). They were related inversely to B.1.427 and B.1.429 (Epsilon) and there was a trend for association with B.1.1.7 (Alpha) lineage. ConclusionDeviations from accurate predictive models are useful for investigating potential immune escape of COVID-19 variants at the population level. The B.1.526 and B.1.526.2 lineages likely have a high potential for immune escape and should be designated as variants of concern. The T95I mutation which is present in the B.1.526, B.1.526.2, and B.1.617.2 (Delta) lineages in the US warrants further investigation as a mutation of concern.