iScience
Volume 25, Issue 10, 21 October 2022, 105237
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Article
Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 SARS-CoV-2 vaccine via non-negative tensor factorization

https://doi.org/10.1016/j.isci.2022.105237Get rights and content
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open access

Highlights

  • Tensor factorization identified 4 components that explain vaccine adverse reactions

  • These components were differently associated with background factors

  • Only 1 component was significantly associated with post-vaccine antibody titer

  • These methods and results will inform future studies on vaccine safety and efficacy

Summary

Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization.

Subject areas

Immunology
computational bioinformatics
machine learning

Data and code availability

Data reported in this paper will be shared by the lead contact upon request. The raw vaccine adverse reaction datasets and source-code used to perform NTF are publicly available at https://github.com/eiryo-kawakami/Vaccine_Tensor_2022_code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Cited by (0)

18

These authors contributed equally

19

Lead contact