@Article{info:doi/10.2196/59467,
author="Slotkin, Rebecca
and Kyriakides, Tassos C
and Yu, Vinni
and Chen, Xien
and Kundu, Anupam
and Gupta, Shaili",
title="CoVimmune COVID-19 Immunity Calculator: Web Application Development and Validation Study",
journal="JMIR Form Res",
year="2025",
month="Apr",
day="22",
volume="9",
pages="e59467",
keywords="COVID-19; immunity; neutralizing antibody; immunoglobulin G; vaccine hesitancy; vaccine timing; patient-centered care; web application; vaccination; SARS-CoV-2",
abstract="Background: This study illustrates the development of a simple web-based application, which demonstrates the relationship between serum anti-SARS-CoV-2 S1/receptor-binding domain immunoglobulin G (IgG) and anti-SARS-CoV-2 neutralizing antibody (nAb) half-maximal inhibitory concentration (IC50) titers in a vaccinated US adult population and compares them to prior data on nAb titers at different time points after vaccination. Objective: The objective of this study is to create an easily accessible calculator that uses the results of commercially available anti-SARS-CoV-2 serum IgG to approximate the underlying ability to neutralize SARS-CoV-2. Methods: Our web-based application leveraged two previously published datasets. One dataset demonstrated a robust correlation between nAb and serum IgG. The other dataset measured nAb titers at specific time periods over a year-long interval following a messenger RNA vaccination primary series and booster vaccine dose. Clinical factors that were statistically significant on a forward linear regression model examining the prediction of nAb from serum IgG were incorporated in the application tool. Results: By combining the datasets described above, we developed a publicly available web-based application that allows users to enter a serum IgG value and determine their estimated nAb titer. The application contextualizes the estimated nAb titer with the theoretical distance from the corresponding vaccine-mediated antibody protection. Using the clinical variables that had a significant impact on how well IgG values predict nAb titers, this application allows for a patient-centered, nAb titer prediction. Conclusions: This application offers an example of how we might bring the advances made in scientific research on protective antibodies post-SARS-CoV-2 vaccination into the clinical sphere with practical tools. ",
issn="2561-326X",
doi="10.2196/59467",
url="https://formative.jmir.org/2025/1/e59467",
url="https://doi.org/10.2196/59467"
}