Back

Stratifying elicited antibody dynamics after two doses of SARS-CoV-2 vaccine in a community-based cohort in Fukushima, Japan

Nakamura, N.; Kobashi, Y.; Kim, K. S.; Tani, Y.; Shimazu, Y.; Zhao, T.; Nishikawa, Y.; Omata, F.; Kawashima, M.; Yoshida, M.; Abe, T.; Saito, Y.; Senoo, Y.; Nonaka, S.; Takita, M.; Yamamoto, C.; Kawamura, T.; Sugiyama, A.; Nakayama, A.; Kaneko, Y.; Park, H.; Jeong, Y. D.; Tatematsu, D.; Akao, M.; Sato, Y.; Iwanami, S.; Fujita, Y.; Wakui, M.; Aihara, K.; Kodama, T.; Shibuya, K.; Iwami, S.; Tsubokura, M.

2022-06-14 infectious diseases
10.1101/2022.06.11.22276266 medRxiv
Show abstract

Recent studies have provided insights into the effect of vaccine boosters on recall immunity. Given the limited global supply of COVID-19 vaccines, identifying vulnerable populations with lower sustained vaccine-elicited antibody titers is important for targeting individuals for booster vaccinations. Here we investigated longitudinal data in a cohort of 2,526 people in Fukushima, Japan, from April 2021 to December 2021. Antibody titers following two doses of a COVID-19 vaccine were repeatedly monitored and information on lifestyle habits, comorbidities, adverse reactions, and medication use was collected. Using mathematical modeling and machine learning, we stratified the time-course patterns of antibody titers and identified vulnerable populations with low sustained antibody titers. Moreover, we showed that only 5.7% of the participants in our cohort were part of the "durable" population with high sustained antibody titers, which suggests that this durable population might be overlooked in small cohorts. We also found large variation in antibody waning within our cohort. There is a potential usefulness of our approach for identifying the neglected vulnerable population.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 8%
18.8%
Similar papers in this journal
2
Nature
○ 645 papers in training set
Top 0.8%
17.2%
Similar papers in this journal
3
Nature Medicine
○ 125 papers in training set
Top 0.1%
12.1%
Similar papers in this journal
4
Cell Discovery
○ 57 papers in training set
Top 0.1%
6.3%
Similar papers in this journal
50% of probability mass above
5
Nature Immunology
○ 79 papers in training set
Top 0.4%
6.3%
Similar papers in this journal
6
Immunity
○ 67 papers in training set
Top 0.5%
3.3%
7
Science Advances
● 1243 papers in training set
Top 13%
2.8%
8
Nature Microbiology
○ 155 papers in training set
Top 1%
2.1%
9
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.6%
2.1%
10
Nature Computational Science
○ 55 papers in training set
Top 0.4%
2.1%
11
Cell
○ 431 papers in training set
Top 5%
1.9%
12
eLife
● 5828 papers in training set
Top 47%
1.9%
13
Science
● 477 papers in training set
Top 5%
1.8%
14
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 27%
1.8%
15
Cell Host & Microbe
126 papers in training set
Top 2%
1.4%
16
Cell Reports
○ 1498 papers in training set
Top 21%
1.4%
17
ACS Nano
● 113 papers in training set
Top 2%
1.1%
18
Protein & Cell
◐ 25 papers in training set
Top 0.4%
1.1%
19
iScience
○ 1154 papers in training set
Top 30%
1.1%
20
Science Translational Medicine
● 127 papers in training set
Top 3%
1.0%
21
Med
○ 39 papers in training set
Top 0.6%
0.9%
22
Cell Reports Medicine
○ 153 papers in training set
Top 6%
0.6%
23
Nature Biotechnology
○ 172 papers in training set
Top 5%
0.6%
24
The Lancet Infectious Diseases
○ 73 papers in training set
Top 1%
0.6%
25
Communications Medicine
○ 113 papers in training set
Top 5%
0.6%

"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.