This document proposes a new Mass-Charge modeling approach to analyze statistical fluctuations in amino acid charges in SARS-CoV-2 variants like Omicron. It introduces a normalized derivation using Excel and Matlab algorithms to examine charge and mass relationships in coronavirus spike proteins. The approach provides insights into the evolving bioinformatic trends affecting infectivity and virulence. Key contributions include a new running semi-covariance notation to analyze non-linear patterns, and integrating genomic data to set up an ending time prediction framework for the pandemic by continent. Results compare SARS-CoV-2 spike protein sequences to other coronaviruses using simplified complex variances. Findings suggest mutations depend on region and that flu virus is closer genetically to rat virus than

1. Predicted COVID-19 pandemic ending for each
continent with Steed covariance
Jun Huang, Zhili Chen, Tommy Zhou, Tong Xu, Moran Huang, Wandong Zhang
Summary
Summary
Summary
Summary
Spectrum-based Mass-Charge modeling has received significant interest in recent years and is
increasingly used in bioinformatic analysis. To explain the statistical phenomenon with positive and
negative fluctuations of amino acid charges in spike protein genomic sequences from SARS-CoV-2 variant
omicron and other coronaviruses, we propose a calculation-based Mass-Charge modeling. Different from
previous semi-covariance co-efficiency method, this study proposes a normalized derivation with the exact
Excel and Matlab tool algorithm. By examining the relative peak and dip of the average with spike protein
genomic sequences of coronaviruses based on the hydrophobic mass to isoelectric charges of the amino
acids, the proposed algorithm provides more clear insights leading to the detail revealing of the underline
evolving trends of bioinformatic entropy of the related infectivity and the virulence. The hydrophobic
interaction is an entropic effect originating from the disruption of the dynamic hydrogen bonds between
molecules of water by the nonpolar molecules forming a clathrate-like structure around. The entropic
effect is from the second law of thermodynamics. The spike proteins from coronaviruses isolated from the
murine for animal and from Omicron, Delta, IHU, OC43, 229E, HKU1, NL63 that cause human infections of
each region were taken as the representative examples in this predictive study.
2022 UCSD Information Theory and Applications Workshop
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2. 1
1
1
1 Introduction and Results
2
2
2
2
Method and Mathematical
Derivation
3
3
3
3 More Results and Conclusion
CONTENTS
Kiha.org.cn

3. CH
1
Introduction and
Results

4. Complex Running Model for Gene Analysis
IEEE Kitchener Waterloo Section
• Different from the existing covariance or correlation
coefficient methods in traditional integer dimension
construction, this study proposes a ReLU based running
pure-inter semi-covariance with the exact Excel tool
algorithm: https://github.com/steedhuang/Poincare-
Fuchs-KleinAutomorphicFunction-COVID19-Mutations
• It involves the fractional center moment extension to
covariance, which ends up as a complex covariance
coefficient that is better than the Pearson correlation
coefficient, in the sense that the nonlinearity relationship
can be further depicted.

5. When it’s ending?
We predict pandemic ending and provide key insights into why certain people die for a
certain mutation and the demographic environment social associated with them.

6. From
From
From
From Year
Year
Year
Year 1880
1880
1880
1880, Karl Pearson's formula
, Karl Pearson's formula
, Karl Pearson's formula
, Karl Pearson's formula

7. To 2003
To 2003
To 2003
To 2003 Nobel Winner
Nobel Winner
Nobel Winner
Nobel Winner
https://www.visionx.org/

8. Contributions
Contributions
Contributions
Contributions
In summary, we make the following contributions in this
research.
• 1. We propose a newly derived notations that can be
used to analyze the non-linear pattern based on a pure
inter running semi-co-variance idea.
• 2. We identify, collect, and integrate data for the latest
pandemic genomic analysis, bearing in mind ending
time prediction framework is to be set up.

9. Table 1: Analysis of Spike Protein from SARS
Table 1: Analysis of Spike Protein from SARS
Table 1: Analysis of Spike Protein from SARS
Table 1: Analysis of Spike Protein from SARS-
-
-
-CoV
CoV
CoV
CoV-
-
-
-2 Wuhan strain in
2 Wuhan strain in
2 Wuhan strain in
2 Wuhan strain in
comparison with the variants of
comparison with the variants of
comparison with the variants of
comparison with the variants of SARS
SARS
SARS
SARS-
-
-
-CoV
CoV
CoV
CoV-
-
-
-2 (
2 (
2 (
2 (Five Continents
Five Continents
Five Continents
Five Continents)
)
)
)

10. Mass-Charge Entropy from Langlands program
IEEE Kitchener Waterloo Section

11. CH
2
Method and
Mathematical
Derivation

12. Problem to solve
For nonlinear relationship like below we don’t want to just take a zero away, we want
to see who made it zero? In what region the relationship is not zero!
How they cancelled each others? What’s different between each zero?

13. Problem here (quadrant correlated)

14. Mathematical Derivation for Steed covariance
• The following simple derivation proves the ReLU relationship
between our upper covariance and Nobel Winner Prof. Harry
M Markowitz's down variance:

15. The pure inter covariance converge and diverge

16. CH
3
More Results and
Conclusion

17. More Results
• To compare and prove the usefulness of the simplified
complex variances, we compare the correlation of SARS-
CoV-2 viral spike protein sequence with other coronavirus
spike protein sequences.
• Since Excel is not capable of handling the imaginary
number, we simplify the calculation with integer power,
but separate the positive and negative covariance signs.
• Because coronaviruses spike proteins have different
electrical charge entropy levels, we find the pure inter
covariance by subtraction of the production of the
respected variances just as the Pearson distance calculation
does.
IEEE Kitchener Waterloo Section

18. IEEE Kitchener Waterloo Section
Mutations are region dependable

19. IEEE Kitchener Waterloo Section
Flu virus is closer to rat virus than Omicron. Year found: