Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population.
As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable d...
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Public Library of Science (PLoS)
2024-05-01
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| Series: | PLOS Digital Health |
| Online Access: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000497&type=printable |
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| author | Naotoshi Nakamura Yurie Kobashi Kwang Su Kim Hyeongki Park Yuta Tani Yuzo Shimazu Tianchen Zhao Yoshitaka Nishikawa Fumiya Omata Moe Kawashima Makoto Yoshida Toshiki Abe Yoshika Saito Yuki Senoo Saori Nonaka Morihito Takita Chika Yamamoto Takeshi Kawamura Akira Sugiyama Aya Nakayama Yudai Kaneko Yong Dam Jeong Daiki Tatematsu Marwa Akao Yoshitaka Sato Shoya Iwanami Yasuhisa Fujita Masatoshi Wakui Kazuyuki Aihara Tatsuhiko Kodama Kenji Shibuya Shingo Iwami Masaharu Tsubokura |
| author_facet | Naotoshi Nakamura Yurie Kobashi Kwang Su Kim Hyeongki Park Yuta Tani Yuzo Shimazu Tianchen Zhao Yoshitaka Nishikawa Fumiya Omata Moe Kawashima Makoto Yoshida Toshiki Abe Yoshika Saito Yuki Senoo Saori Nonaka Morihito Takita Chika Yamamoto Takeshi Kawamura Akira Sugiyama Aya Nakayama Yudai Kaneko Yong Dam Jeong Daiki Tatematsu Marwa Akao Yoshitaka Sato Shoya Iwanami Yasuhisa Fujita Masatoshi Wakui Kazuyuki Aihara Tatsuhiko Kodama Kenji Shibuya Shingo Iwami Masaharu Tsubokura |
| author_sort | Naotoshi Nakamura |
| collection | DOAJ |
| description | As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an "antibody score", which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics. |
| format | Article |
| id | doaj-art-abbce656ef634b7abbb9954e4238df34 |
| institution | DOAJ |
| issn | 2767-3170 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-abbce656ef634b7abbb9954e4238df342025-08-20T02:48:57ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702024-05-0135e000049710.1371/journal.pdig.0000497Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population.Naotoshi NakamuraYurie KobashiKwang Su KimHyeongki ParkYuta TaniYuzo ShimazuTianchen ZhaoYoshitaka NishikawaFumiya OmataMoe KawashimaMakoto YoshidaToshiki AbeYoshika SaitoYuki SenooSaori NonakaMorihito TakitaChika YamamotoTakeshi KawamuraAkira SugiyamaAya NakayamaYudai KanekoYong Dam JeongDaiki TatematsuMarwa AkaoYoshitaka SatoShoya IwanamiYasuhisa FujitaMasatoshi WakuiKazuyuki AiharaTatsuhiko KodamaKenji ShibuyaShingo IwamiMasaharu TsubokuraAs we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an "antibody score", which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000497&type=printable |
| spellingShingle | Naotoshi Nakamura Yurie Kobashi Kwang Su Kim Hyeongki Park Yuta Tani Yuzo Shimazu Tianchen Zhao Yoshitaka Nishikawa Fumiya Omata Moe Kawashima Makoto Yoshida Toshiki Abe Yoshika Saito Yuki Senoo Saori Nonaka Morihito Takita Chika Yamamoto Takeshi Kawamura Akira Sugiyama Aya Nakayama Yudai Kaneko Yong Dam Jeong Daiki Tatematsu Marwa Akao Yoshitaka Sato Shoya Iwanami Yasuhisa Fujita Masatoshi Wakui Kazuyuki Aihara Tatsuhiko Kodama Kenji Shibuya Shingo Iwami Masaharu Tsubokura Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. PLOS Digital Health |
| title | Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. |
| title_full | Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. |
| title_fullStr | Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. |
| title_full_unstemmed | Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. |
| title_short | Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population. |
| title_sort | modeling and predicting individual variation in covid 19 vaccine elicited antibody response in the general population |
| url | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000497&type=printable |
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