Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV - a machine learning approach

Aug 6, 2026

SARS-CoV-2 mRNA vaccines induce strong immunity in people living with HIV on effective antiretroviral therapy, yet the determinants of vaccine-driven humoral responses remain debated. This study builds a machine learning model to predict antibody responses after primary and booster mRNA vaccination and to evaluate how demographic and clinical variables shape antibody production across time. Longitudinal data from 497 participants are used to compare approaches and quantify variable influence on immunogenicity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusions

Background

Study context in people living with HIV

Montesi et al. Journal of Translational Medicine (2024) 22:432 [https://doi.org/10.1186/s12967-024-05147-1](https://doi.org/10.1186/s12967-024-05147-1) Journal of Translational Medicine RESEARCH Open Access Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach Giorgio Montesi1†, Matteo Augello2†, Jacopo Polvere 1, Giulia Marchetti2, Donata Medaglini 1* and Annalisa Ciabattini1* Abstract Background SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART) . However, whether viro-immunologic parameters or other factors affect immune responses to vaccination is debated. This study aimed to develop a machine learning-based model able to predict the humoral response to mRNA vaccines in PLWH and to assess the impact of demographic and clinical variableson antibody production over time. Methods Different machine learning algorithms have been compared in the setting of a longitudinal observational study involving 497 PLWH, after primary and booster SARS-CoV-2 mRNA vaccination. Both Generalized Linear Models and non-linear Models (Tree Regression and Random Forest) were trained and tested. Results Non-linear algorithms showed better ability to predict vaccine-elicited humoral responses. The bestperforming Random Forest model identified a few variables as more influential, within 39 clinical, demographic, and immunological factors. In particular, previous SARS-CoV-2 infection, BMI, CD4 T-cell count and CD4/CD8 ratio were positively associated with the primary cycle immunogenicity, yet their predictive value diminished with the administration of booster doses. Conclusions In the present work we have built a non-linear Random Forest model capable of accurately predicting humoral responses to SARS-CoV-2 mRNA vaccination, and identifying relevant factors that influence the vaccine response in PLWH. In clinical contexts, the application of this model provides promising opportunities for predicting individual vaccine responses, thus facilitating the development of vaccination strategies tailored for PLWH. Keywords Machine learning, SARS-CoV-2, HIV, Statistical modeling, Vaccines, mRNA, Antibodies, Immune response, ImmunoVirology †Giorgio Montesi and Matteo Augello equally contributed. *Correspondence: Donata Medaglini [donata.medaglini@unisi.it](donata.medaglini@unisi.it)[ ](donata.medaglini@unisi.it)Annalisa Ciabattini[annalisa.ciabattini@unisi.it](annalisa.ciabattini@unisi.it) Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativeco](http://creativeco)[mmons.org/publicdomain/zero/1.0/](mmons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

FAQs

What is the study’s main goal?

To develop a machine learning model that predicts humoral (antibody) responses to SARS-CoV-2 mRNA vaccination in people living with HIV after primary and booster doses.

Which data and methods are used?

The study uses longitudinal observational data from 497 participants and compares generalized linear models with non-linear models such as tree regression and random forest.

What factors were most influential for antibody responses?

The best random forest model highlighted a small set of variables, including prior SARS-CoV-2 infection, BMI, CD4 T-cell count, and CD4/CD8 ratio, with diminished predictive value after booster administration.

Aug 6, 2026

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