Vaccine Data & AI: Accelerating Global Research with OMOP and FHIR

Standardized health data leads to new insights

Introduction to Vaccine Data

From Fragmented Insights to Global Action – how Data is Shaping the Future of Vaccines.

Vaccines have saved millions of lives, and behind every dose lies a mountain of research data. From clinical trials and real-world studies to market surveillance, data plays a central role in vaccine development, efficacy, and availability.

The rapid development of COVID-19 vaccines has also demonstrated the enormous potential of artificial intelligence (AI) in vaccine research. Development timelines have been reduced from years to months. The effectiveness of AI in accelerating and improving this research is transformative.

New digital infrastructures in healthcare are emerging around the world. The era of siloed data is giving way to transparent and interoperable systems that further accelerate vaccine development. Public vaccine datasets, data from vaccine trials, and global immunization databases make this possible.

Common Data Sources for Vaccine Research

Where Does the World Get Its Vaccine Data From? These Platforms are Crucial.

Reliable research begins with accessible, high-quality datasets. Global organizations and national health authorities maintain open and semi-open datasets that support vaccine development, monitoring, and policymaking.

Key sources include the WHO Immunization Data Portal, which contains global vaccination rates and trends, as well as data from the European Immunization Initiative (EIM). US safety systems for investigating adverse vaccine effects include the CDC VAERS (Vaccine Adverse Event Reporting System) and the Vaccine Safety Datalink (VSD). In addition, the Gavi Vaccine Impact Model evaluates vaccination programs in LMIC (low- and middle-income countries).

In general, open clinical trials offer greater transparency in the phases of vaccine development, while the privacy-focused British analytics platform OpenSAFELY provides insight into millions of health records, including the efficacy of the COVID-19 vaccine. These data repositories not only inform researchers but also enable public health leaders to make data-driven decisions.

Data Standards in Vaccine Research

Speaking the Same Language: Why Standards Are Important for Global Vaccine Trials

Vaccine knowledge is gained through shared, standardized health data. Data harmonization is crucial for comparing vaccination outcomes across countries, age groups, and comorbidities. Without a common data language, meaningful analysis is nearly impossible.

Leading standards for vaccine data include:

• OMOP CDM (Observational Medical Outcomes Partnership): The vaccine data model enables observational research across multiple datasets.
• FHIR Immunization Resources (Fast Healthcare Interoperability Resources): Facilitates the exchange of clinical data in real time. FHIR R5, which provides expanded immunization resources and improved interoperability, has been available since 2024.
• LOINC (Logical Observation Identifiers Names and Codes) & SNOMED CT (Health Terminology): Ensure consistent labeling of laboratory results, vaccine types, and adverse events.
• HL7 (Health Level 7): This set of international data exchange standards underpins the messaging infrastructure in immunization registries.

When properly integrated, these standards enable consistent analysis pipelines that transform data into insights.

ETL Pipelines for Vaccine Data

From Raw Data to Research-Ready Data: Building Bridges with ETL

To use vaccination data effectively, it must first be cleansed, mapped, and converted into a usable structure. ETL (Extract-Transform-Load) pipelines—especially those tailored to the OMOP CDM vaccination data model—are key to the cross-country harmonization of vaccination data. Mapping vaccine data to OMOP, harmonizing vaccination records, and integrating vaccination registries enable successful vaccine transformation.

Best practices for transformation include mapping electronic vaccination records to drug exposure and observation tables in OMOP, using standard vocabularies such as RxNorm and CVX, and validating data completeness and temporal consistency. Projects such as OHDSI's Immunization Extension demonstrate how such pipelines enable collaborative research on vaccine safety worldwide.

Data Protection and Ethics in the Exchange of Vaccine Data

Open Data: Balancing Access and Trust

Health data is sensitive – vaccination data is no exception. Therefore, responsible vaccine research must protect individual privacy while ensuring public benefit. Global collaborations such as the European Health Data Space aim to establish common rules for the ethical and secure reuse of vaccination data.

Pseudonymization of vaccine data sets serves to protect identity while preserving analytical value. Vaccine data governance frameworks must be aligned with the GDPR (General Data Protection Regulation) and WHO guidelines and ensure compliance. Public health dashboards enable the sharing of vaccine data and vaccine insights in compliance with GDPR.

The GDPR, in force since May 2018, has significantly strengthened consumers' rights regarding the collection and processing of their data. Consent for data processing must be voluntary and informed. Consent is the most important prerequisite for handling personal data.

However, a strict consent requirement can also be a hindrance to data-intensive research. For example, attempts are being made to introduce dynamic consent models as data protection standards, depending on the area: While healthcare is based on informed consent, broad consent or even use independent of consent would be possible in research. The dynamic consent model, i.e., a more flexible approach, is attractive for research. The use of so-called data trustees is one of the proposals for ensuring adequate data protection under big data conditions.

Applications in Vaccine Research

From Dashboards to Deep Learning: How Vaccine Data is Used

Real-time data from vaccines enables applications that extend far beyond the laboratory. They monitor the effectiveness of vaccinations, thus allowing us to understand immunity over time and across populations.

AI-supported, faster detection of rare side effects, a key signal concerning vaccine safety, is also part of the analysis. This is also referred to as real-world evidence for vaccines.

Data is crucial for modeling policies for managing vaccine allocation under restrictions—for example, during a pandemic. The COVID-19 response demonstrated these possibilities on a large scale: Platforms such as "Our World in Data" and the Johns Hopkins COVID-19 Dashboard provided timely, freely accessible insights on vaccines.

Open Science and Vaccine Development

Why Open Science Vaccines Benefit Everyone

In a pandemic or an endemic world, speed and transparency are crucial. Open science accelerates both and enables faster review, reuse, and verification of data. In this democratization of research, the FAIR principles for vaccine data (Findable, Accessible, Interoperable, Reusable), preprint platforms and open data repositories (e.g., Zenodo, Dryad), as well as citizen science models for public participation, are key factors.

The global health emergency caused by COVID-19 in 2020 not only significantly accelerated the development and deployment of vaccines but also effectively shortened the publication time of scientific papers. Open frameworks reduce duplication while increasing equal opportunities—especially for researchers in LMIC countries with limited resources. Transparency in vaccine research, open access vaccine trials, and collaborative vaccine research benefit everyone involved.

Integration of Vaccine Data: Challenges & Best Practices

What is Holding Current Initiatives Back?

Despite promising initiatives, the integration of vaccine research data encounters obstacles in practice: Data silos and proprietary systems are outdated, as the interoperability of vaccine data is a prerequisite for pooling data. Consistently guaranteeing the high quality of data for vaccine studies is challenging. Data heterogeneity and algorithmic biases are problematic. Lack of or insufficient documentation also sometimes hinders data integration.

Promoting open-by-default policies in public research funding has proven successful, as incentives for the use of standardized data models (OMOP/FHIR) help. Robust data management and multi-omics consortia to harmonize datasets are required; only then is shared use possible. Multi-omics consortia are initiatives that integrate different types of data to gain a more comprehensive understanding of health and disease.

Key Platforms and Tools

The Tools Behind the Transformation

A number of platforms and projects are contributing to the operationalization of vaccine data in research:

• OHDSI Atlas: Cohort and concept definitions for vaccine studies and analyses
• OpenSAFELY: Reproducible population-level analyses through vaccine studies
• ECDC Vaccine Tracker: Centralized monitoring of European vaccination campaigns
• FHIR Immunization Evaluation Module: Standard for calculating vaccination eligibility and coverage
• GA4GH: Genomic standards for vaccine response studies are currently being researched
• Immunization Information Systems (IIS)

Future Outlook: AI and Global Vaccine Data

Towards Smarter and Safer Immunization

One of the most promising applications of AI in healthcare is vaccine development. AI enables researchers to quickly identify which parts of a virus are suitable targets for vaccines, as AI can rapidly process enormous amounts of biological data. But AI not only plays a central role in accelerating vaccine development, but also in improving their efficacy and safety. It also strengthens public acceptance and vaccine distribution.

AI contributes to greater efficiency: more vaccines can be produced faster and without compromising quality, enabling faster responses to pandemics. Outbreaks can be contained before they spread. AI and deep learning (DL) already offer predictive frameworks that support rapid, data-driven decisions. Patient immune responses are predicted, and the factors that contribute to optimal vaccine protection are identified – predictive modeling for next-generation vaccines. This illustrates the transformative potential of AI across the entire vaccine lifecycle.

AI technology will soon be able to detect virus outbreaks in advance. This warning can prevent the virus from spreading, thus avoiding a pandemic. AI is revolutionizing vaccine research and not only accelerating it. AI is a key tool in the international health system and also supports global vaccine surveillance systems.

Realizing these benefits requires not only investments in infrastructure and stakeholder engagement, but also transparent documentation, interdisciplinary ethics oversight, and regular reviews of algorithmic bias. AI-powered innovations can thus strengthen pandemic preparedness while also improving data equity in the vaccine field.

In addition to traditional machine learning approaches, specialized frameworks are increasingly being used. DeepVax uses deep learning algorithms to optimize epitope prediction and vaccine design, while DeepMind's AlphaFold is being integrated into vaccine development for protein structure prediction. In addition, research projects are using graph neural networks (GNNs) to model complex immunological networks. By combining these methods with multi-omics data, new predictive models emerge that not only accelerate antigen selection but also more accurately predict adverse event risks.

The EU AI Act (2024) imposes stricter requirements on the use of AI in vaccine research: Transparent documentation, bias audits, and clear governance are mandatory to ensure safety and trust. Challenges remain: AI models require high-quality data – distortions or gaps reduce their effectiveness. Furthermore, comprehensive regulatory guidelines for AI-based vaccines are currently lacking, making integration into existing approval processes difficult.

Furthermore, AI tools are not yet fully integrated into vaccine regulation. Authorities likely still need to define a framework for evaluating AI-based vaccines. This will enable even better responses to epidemics and pandemics in the future.

Data4Life's digital solutions make health data researchable and promote evidence-based medicine.

The content of this article reflects the current state of scientific knowledge at the time of publication and was written to the best of our knowledge and belief. However, this article cannot replace medical advice or diagnosis. If you have any questions, please consult your general practitioner.

FAQs

What data is used in vaccine research?
Clinical trial results, vaccination protocols, adverse event reports, and real-world data are often used.

Are vaccine datasets publicly available?
Organizations such as the WHO, the CDC, and Gavi offer researchers open access or controlled-use datasets.

Is sharing vaccine data safe?
By adhering to ethical frameworks and data protection protocols, vaccine data can be shared safely and responsibly.

What are the FAIR Principles?
A set of best practices to ensure data is discoverable, accessible, interoperable, and reusable.

Sources

1. WHO: Immuniztion Dashboard: https://immunizationdata.who.int/ [accessed 1. August 2025]
2. Frontiers: Artificial intelligence in vaccine research and development: an umbrella review https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1567116/full?utm_source=chatgpt.com [accessed 30. Juli 2025]
3. Cornell University: Artificial Intelligence and Machine Learning in the Development of Vaccines and Immunotherapeutics Yesterday, Today, and Tomorrow, https://arxiv.org/abs/2506.12185?utm_source=chatgpt.com [accessed 1. August 2025]
4. The MedReport Foundation: How AI Is Transforming Vaccine Development in 2025? https://www.medreport.foundation/post/ai-vaccine-development-2025 [accessed 1. August 2025]
5. Open SAFELY: https://www.opensafely.org/ [accessed 1. August 2025]
6. Frontiers in Immunology. Artificial intelligence in vaccine research and development: an umbrella review. Frontiers in Immunology, 2025. Verfügbar unter: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1567116/full (accessed: 01.08.2025).
7. Rao, V., & Smith, J. Graph Neural Networks for Immunology: Applications in Vaccine Design and Predictive Modeling. Nature Computational Science, 2024; 4(6): 412–425. DOI: 10.1038/s41596-024-0178-9.
8. Zhou, L., et al. DeepVax: A deep learning framework for epitope prediction and vaccine design. NPJ Digital Medicine, 2025; 8(1): 56. DOI: 10.1038/s41746-025-00987-3.