A Hybrid Computational Framework for Intelligent Inter- continent SARS-CoV-2 Sub-strains Characterization and Prediction

Abstract

Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speaks for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining from enriched genome datasets and output classification targets, helped intelligent prediction of emerging or new viral sub-strains. Classification results outsmarted state-of-the-art methods and sustained an increase in sub-strains within the various continents with nucleotide mutations dynamically varying between individuals in close association with the virus adaptability to its host/environment. They also offer explanations for the growing concerns and next wave(s) of the virus. Defuzzifying confusable pattern clusters for comparative performance with the proposed cognitive solution is a possible future research direction of this paper.