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.
Ikeda Kei, Nakada Taka-Aki, Kageyama Takahiro, Tanaka Shigeru, Yoshida Naoki, Ishikawa Tetsuo, Goshima Yuki, Otaki Natsuko, Iwami Shingo, Shimamura Teppei, Taniguchi Toshibumi, Igari Hidetoshi, Hanaoka Hideki, Yokote Koutaro, Tsuyuzaki Koki, Nakajima Hiroshi, Kawakami Eiryo
Adverse reaction, COVID19, Computational phenotyping, Tensor factorization, mRNA vaccine