Dataset Information

Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts.

ABSTRACT:

Introduction

The rVSVDG-ZEBOV-GP (Ervebo®) vaccine is both immunogenic and protective against Ebola. However, the vaccine can cause a broad range of transient adverse reactions, from headache to arthritis. Identifying baseline reactogenicity signatures can advance personalized vaccinology and increase our understanding of the molecular factors associated with such adverse events.

Methods

In this study, we developed a machine learning approach to integrate prevaccination gene expression data with adverse events that occurred within 14 days post-vaccination.

Results and discussion

We analyzed the expression of 144 genes across 343 blood samples collected from participants of 4 phase I clinical trial cohorts: Switzerland, USA, Gabon, and Kenya. Our machine learning approach revealed 22 key genes associated with adverse events such as local reactions, fatigue, headache, myalgia, fever, chills, arthralgia, nausea, and arthritis, providing insights into potential biological mechanisms linked to vaccine reactogenicity.

SUBMITTER: Gonzalez Dias Carvalho PC

PROVIDER: S-EPMC10663260 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts.

Gonzalez Dias Carvalho Patrícia Conceição PC Dominguez Crespo Hirata Thiago T Mano Alves Leandro Yukio LY Moscardini Isabelle Franco IF do Nascimento Ana Paula Barbosa APB Costa-Martins André G AG Sorgi Sara S Harandi Ali M AM Ferreira Daniela M DM Vianello Eleonora E Haks Mariëlle C MC Ottenhoff Tom H M THM Santoro Francesco F Martinez-Murillo Paola P Huttner Angela A Siegrist Claire-Anne CA Medaglini Donata D Nakaya Helder I HI

Frontiers in immunology 20231108

<h4>Introduction</h4>The rVSVDG-ZEBOV-GP (Ervebo®) vaccine is both immunogenic and protective against Ebola. However, the vaccine can cause a broad range of transient adverse reactions, from headache to arthritis. Identifying baseline reactogenicity signatures can advance personalized vaccinology and increase our understanding of the molecular factors associated with such adverse events.<h4>Methods</h4>In this study, we developed a machine learning approach to integrate prevaccination gene expre ...[more]

PMID: 38022684

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Project description:BackgroundAlthough the COVID-19 pandemic has left an unprecedented impact worldwide, countries such as the United States have reported the most substantial incidence of COVID-19 cases worldwide. Within the United States, various sociodemographic factors have played a role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between US counties, underscoring the need for efficient and accurate predictive modeling strategies to inform public health officials and reduce the burden on health care systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the United States, vaccination rates have become stagnant, necessitating predictive modeling to identify important factors impacting vaccination uptake.ObjectiveThis study aims to determine the association between sociodemographic factors and vaccine uptake across counties in the United States.MethodsSociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases such as the US Centers for Disease Control and Prevention and the US Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data.ResultsOur model predicted COVID-19 vaccination uptake across US counties with 62% accuracy. In addition, it identified location, education, ethnicity, income, and household access to the internet as the most critical sociodemographic features in predicting vaccination uptake in US counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by health care authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns.ConclusionsOur study reveals that sociodemographic characteristics are predictors of vaccine uptake rates across counties in the United States and, if leveraged appropriately, can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.

Dataset Information

Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts.

Introduction

Methods

Results and discussion

Publications

Baseline gene signatures of reactogenicity to Ebola vaccination: a machine learning approach across multiple cohorts.

Similar Datasets

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