Back

Estimated Spike Evolution and Impact of Emerging SARS-CoV-2 Variants

Lu, Y.; Han, K.; Xue, G.; Zheng, N.; Jin, G.

2021-05-10 infectious diseases
10.1101/2021.05.06.21256705 medRxiv
Show abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, has been mutating and thus variants emerged. This suggests that SARS-CoV-2 could mutate at an unsteady pace. Supportive evidence comes from the accelerated evolution which was revealed by tracking mutation rates of the genomic location of Spike protein. This process is sponsored by a small portion of the virus population but not the largest viral clades. Moreover, it generally took one to six months for current variants that caused peaks of COVID-19 cases and deaths to survive selection pressure. Based on this statistic result and the above speedy Spike evolution, another upcoming peak would come around July 2021 and disastrously attack Africa, Asia, Europe, and North America. This is the prediction generated by a mathematical model on evolutionary spread. The reliability of this model and future trends out of it comes from the comprehensive consideration of factors mainly including mutation rate, selection course, and spreading speed. Notably, if the prophecy is true, then the new wave will be the first determined by accelerated Spike evolution.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
iScience
○ 1154 papers in training set
Top 0.2%
11.7%
Similar papers in this journal
2
Cell Discovery
○ 57 papers in training set
Top 0.1%
7.7%
Similar papers in this journal
3
eLife
● 5828 papers in training set
Top 20%
6.1%
Similar papers in this journal
4
Journal of Medical Virology
○ 140 papers in training set
Top 0.3%
5.4%
Similar papers in this journal
5
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 13%
4.0%
6
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 0.5%
4.0%
7
Virus Evolution
◐ 155 papers in training set
Top 0.6%
3.2%
8
Science Advances
● 1243 papers in training set
Top 11%
3.2%
9
PNAS Nexus
◐ 159 papers in training set
Top 0.4%
2.6%
10
Molecular Biology and Evolution
◐ 542 papers in training set
Top 2%
2.6%
50% of probability mass above
11
Scientific Reports
○ 3612 papers in training set
Top 39%
2.6%
12
PLOS Pathogens
● 820 papers in training set
Top 5%
2.4%
13
Microbiology Spectrum
● 469 papers in training set
Top 6%
2.1%
14
Protein & Cell
◐ 25 papers in training set
Top 0.2%
2.1%
15
Cell Host & Microbe
126 papers in training set
Top 1%
2.1%
16
mBio
● 833 papers in training set
Top 7%
1.9%
17
The Innovation
○ 13 papers in training set
Top 0.1%
1.9%
18
Frontiers in Microbiology
○ 427 papers in training set
Top 5%
1.9%
19
Frontiers in Public Health
○ 148 papers in training set
Top 4%
1.7%
20
Nature Communications
○ 5641 papers in training set
Top 45%
1.7%
21
PLOS Computational Biology
● 1863 papers in training set
Top 14%
1.7%
22
Med
○ 39 papers in training set
Top 0.3%
1.4%
23
Science China Life Sciences
○ 29 papers in training set
Top 0.3%
1.4%
24
Viruses
○ 332 papers in training set
Top 4%
1.1%
25
Cell Reports
○ 1498 papers in training set
Top 23%
1.1%
26
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 5%
1.1%
27
Journal of Genetics and Genomics
○ 38 papers in training set
Top 0.6%
1.0%
28
PLOS Biology
● 486 papers in training set
Top 9%
1.0%
29
Virologica Sinica
○ 11 papers in training set
Top 0.2%
1.0%
30
Frontiers in Medicine
○ 120 papers in training set
Top 4%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.