From data to action: How AI and digital twins are changing face of immunization programs

Researchers have unveiled a scalable digital twin system aimed at transforming the way primary healthcare networks manage immunization processes.

Their work, titled “Simulation-based assessment of digital twin systems for immunisation,” was published in Frontiers in Digital Health in August 2025, highlighting a data-driven approach to boost vaccine coverage and reduce wastage while maintaining equity and operational efficiency.

Transforming vaccine delivery through digital twins

The study introduces TwinVax, a cloud-ready, standards-based architecture that leverages digital twin technology to streamline the critical operations of vaccine storage, distribution, and administration in primary care facilities. Built upon the ISO 23247 digital twin framework, TwinVax adapts principles originally used in industrial manufacturing to meet the demands of healthcare delivery, offering precision, real-time data visibility, and predictive insights.

The system is designed to monitor both cold-chain conditions and population-level vaccination coverage, two persistent challenges in global immunization programs. By integrating continuous sensor data from ice-lined refrigerators (ILRs), portable thermal boxes, and electronic health records, TwinVax provides real-time dashboards and predictive tools for decision-makers, allowing healthcare providers to take timely, targeted actions to safeguard vaccine integrity and improve coverage.

Key components of the architecture include an observable domain to capture field data, a data control and collection layer for device and environment monitoring, a digital twin platform for processing and predictive modeling, and a user domain for actionable insights and decision support. The architecture leverages AWS services, MQTT protocols, and time-series databases like InfluxDB and TimescaleDB, ensuring scalability and seamless integration with existing health IT systems.

Operational insights and predictive accuracy

TwinVax can generate actionable intelligence across the immunization value chain. By continuously analyzing temperature data and correlating it with vaccination records, the system proactively alerts healthcare workers to cold-chain failures or upcoming coverage gaps. This feature is crucial in preventing potency loss due to temperature excursions, ensuring that every dose delivered retains its intended efficacy.

Moreover, the platform supports predictive analytics for vaccine coverage, identifying individuals and communities at risk of missing their scheduled doses. By incorporating socio-economic and clinical predictors, such as maternal education, household income, and antenatal care indicators, the system provides a sophisticated risk stratification model. Machine learning algorithms, including decision trees, support vector machines, random forests, and gradient boosting methods, enhance forecasting accuracy, empowering primary care teams to design data-driven outreach campaigns.

The research team validated the architecture using discrete-event simulation models implemented in SimPy. They modeled real-world workflows, tested system responses under normal and adverse conditions, and incorporated population data from a health region in Rio de Janeiro. This rigorous simulation approach demonstrated that the system could effectively handle adverse scenarios such as intermittent connectivity or cold-chain disruptions, offering a high degree of reliability for real-world implementation.

Ethical, secure and scalable healthcare innovation

Security and ethical governance are at the heart of TwinVax. The architecture employs robust encryption, stringent access controls, and comprehensive audit mechanisms, ensuring compliance with international data protection standards, including the General Data Protection Regulation (GDPR). By embedding human oversight into all automated decision-making processes, the platform mitigates risks of bias or inequitable service delivery, reinforcing trust among both patients and healthcare providers.

Equity is another critical dimension of the system. By combining real-time analytics with predictive modeling, TwinVax ensures that underserved and vulnerable populations are not left behind in immunization campaigns. Automated notifications for patients, coupled with operational support for healthcare workers, enable proactive interventions, bridging gaps in service delivery and improving overall coverage rates.

While the simulation results are promising, the researchers emphasize the need for real-world pilot implementations to validate system performance at scale. Practical challenges such as integrating heterogeneous electronic health records, maintaining stable connectivity in remote locations, and periodically recalibrating predictive models for changing demographics remain critical next steps.

The study underscores the potential of digital twin systems as a reference model for national and regional immunization programs, particularly in settings striving to modernize their healthcare delivery frameworks. By offering modularity, scalability, and interoperability, TwinVax provides a blueprint that can be adapted to diverse healthcare ecosystems worldwide.

Looking forward, the authors highlight the importance of interdisciplinary collaboration to enhance adoption and sustainability. Partnerships among technology developers, public health authorities, and regulatory agencies will be essential for building trust and ensuring that such systems are tailored to the unique needs of different healthcare contexts. Continuous research and field-level evaluations will also be crucial in refining the system’s predictive capabilities and operational efficiency.