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

Ray-Ming Chen; Ray-Ming Chen

doi:10.3934/mbe.2021232

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4572-4585. doi: 10.3934/mbe.2021232

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Research article Special Issues

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

Ray-Ming Chen ^,

Department of Mathematics and Statistics, Baise University, 21 Zhongshan No.2 Road, Baise 533000, China

Academic Editor: Xiangtao Li

Received: 21 March 2021 Accepted: 17 May 2021 Published: 26 May 2021

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.
- mutant variants,
- UK COVID-19,
- distance tensor product,
- 3-valued features,
- feature extraction
Citation: Ray-Ming Chen. Track the dynamical features for mutant variants of COVID-19 in the UK[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4572-4585. doi: 10.3934/mbe.2021232

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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.

References

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