Track the dynamical features for mutant variants of COVID-19 in the UK

Abstract

Aims: The main purpose of this study is to explore whether the new variant SARS-CoV-2 VOC 202012/01 in the UK is equipped with some leading or underlying features. Methods: We apply a systematic and persuasive approach to reveal the underlying dynamical features of this variant. The approach utilises extracting the main features, which consist of 3-valued features, via the time-series data for new cases, 28-day deaths and 60-day deaths. The experimental samples chosen rely on the the rolling sets of regional data vectors whose dimensions are all 7 days. These data sets are projected onto the 3-valued features to yield the vector rejections. Then the minimal features are thus extracted by the minimal total norms. Then we map out the traces of the similarities between all the extracted time-varying features. Results: Our findings, no matter in preliminary or follow-up study, clearly show there is no consistent and substantial shift in the 3-valued features even after the occurrence of this new variant - this might validate the efficacy of the current vaccines against this variant. Conclusions: Since the underlying features of the mutant is unchanged, and the leading feature of B1.1.7 is not yet present, it might help us make the lockdown decision "choose the lesser of the two evils: pandemic and economic woes", and validate vaccine developments or adopt preventive measures.

Keywords

• mutant variants
• uk covid-19
• distance tensor product
• 3-valued features
• feature extraction