Prediction of vaccine-induced antibody dynamics from 1 or 2 blood samplings using mathematical models and machine learning and search for optimal blood sampling schedules

Abstract

While biomedical data is highly accurate, the amount of data is limited, and there is a need to develop analytical methods that effectively utilize a small amount of data. In this study, we used data collected from approximately 2,500 individuals in the Fukushima vaccine cohort, Japan's largest and longest cohort for the COVID-19 vaccine. By applying an integrated approach of mathematical models and machine learning, we estimated IgG(S) antibody titer dynamics from 1 or 2 IgG(S) antibody titer data, age, and sex. This means that IgG(S) antibody titer data at any given time can be predicted from 1 or 2 blood samples. Furthermore, we researched the optimal timing of blood sampling to use this approach effectively. This approach can also be applied to speeding up clinical trials and fundamental research where data acquisition is difficult.