Quantitative analysis of antibody response induced by COVID-19 vaccine

Reference No.	2024a029
Type/Category	Grant for Young Researchers and Students- Short-term Visiting Researcher
Title of Research Project	Quantitative analysis of antibody response induced by COVID-19 vaccine
Principal Investigator	Hyeongki Park（Division of Natural Science, Nagoya University・Assistant Professor）
Research Period	May 14,2024. ～ May 17,2024. July 18,2024. ～ July 20,2024.
Keyword(s) of Research Fields	Antibody response, SARS-CoV-2, mathematical model, machine leraning
Abstract for Research Report	The global pandemic of SARS-CoV-2 virus that emerged in 2019 posed a major threat to humanity. At the same time, however, it also left behind the largest scale of virus-related high-quality data in human history. Analyzing these dataset is very important not only from an academic perspective, but also to prepare for SARS-CoV-2 virus and other emerging infectious disease that may occur in the future. In this study, we analyze in detail the accumulated large amount of data on COVID-19 mRNA vaccine-induced antibody titers. In particular, we incorporate a combined mathematical modeling and machine learning approach to quantitatively analyze antibody dynamics at the individual level. Specifically, we introduce a mathematical model describing the immune process by which antibodies are produced by mRNA vaccination and fitted this model to the real-world data to reconstruct the time-series antibody titers at the individual level. A number of features, such as the peak antibody titiers, duraion of antibody shedding, are calculated from the reconstructed curves to express the antibody dynamics for each individual as a high-dimensional vector. The antibody dynamics of all cases are then visualized on a two-dimensional plane using a dimension reduction technique. This makes it possible to observe the variation in antibody dynamics due to multiple vaccinations at the individual and population levels. Furthermore, we consider using machine learning methods such as PLS to construct a shared latent space in which this set of 2D vectors forms a vector field. Through this study, we expect to gain quantitative insights into the dynamics of COVID-19 mRNA vaccine-induced antibody titers. The construction of a shared latent space to form a vector field is also expected to make theoretical progress since it is new from a theoretical point of view.
Organizing Committee Members (Workshop) Participants (Short-term Joint Usage)	Hyeongki Park（Nagoya University・Assistant Professor） Mito Miyake（Nagoya University・B4） Shizuo Kaji（Kyushu University・Professor）