Serological evaluation and antibody prediction model for inactivated COVID-19 vaccination in school children

Abstract

ObjectiveTo determine the serum antibody level and risk factors in the adolescent population in a county in Zhejiang Province， following the immunization with inactivated COVID-19 vaccine， and to construct a prediction model for antibody concentration.MethodsWe conducted the study in a county in Zhejiang Province， employing a stratified cluster random sampling strategy in school children who had received the inactivated COVID-19 vaccine. Data on gender， age， type of vaccine， and time of vaccination was collected. Serum samples were also collected to test for anti-S and N IgG antibody against the SARS-CoV-2 by using chemiluminescent immunoassay （CLIA）. Risk factors were determined to construct a prediction model for antibody concentration.ResultsThe IgG antibody concentration was significantly higher in girls， those who received two doses， and those who had simply received the KX vaccine . It decreased with age and time interval between the sampling and last vaccination. The prediction model constructed by random forest regression in the study had a better model fit and predictive ability than that by the multivariable linear stepwise regression.ConclusionGender， age， vaccination dose， type of vaccine， and time of vaccination are associated with vaccination effectiveness of inactivated COVID-19 vaccines in adolescents. Prediction model could predict the antibody level in the vaccinated population， which can provide a new tool for better evaluation of vaccination effectiveness against emerging infectious diseases in future.

Keywords

• sars-cov-2
• inactivated vaccine
• school children
• vaccination effectiveness
• serological evaluation
• prediction model