Most NITAGS use the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) method to evaluate the quality of evidence and make recommendations (12). Figure 2 illustrates the GRADE approach to rating the quality of evidence.

The quality of evidence is based on the research methodology’s ability to remove or control for confounding and bias. For example, data from randomised controlled trials (RCTs) are of high quality, and observational studies are of low quality according to the GRADE ranking (12, 13).

In this approach, RCTs are the golden standard for evidence quality. However, RCT findings are often less generalisable to the real world due to the study’s strict inclusion/exclusion criteria (lack of external study validity) (13). RCTs can also be misinterpreted; for example, if event-driven RCTs are analysed as if they were evaluating incidence rates, it could result in overestimating the vaccine’s effectiveness (14).

Expert opinion is considered low-quality evidence, yet most emerging infectious diseases are discovered because clinicians notice abnormalities (15). The timeliness of decision-making can be hindered by waiting for sufficiently strong GRADE evidence.

Real-world data offers an essential complement to RCT data, spanning more diverse population profiles and vaccination administration scenarios. However, large-scale data is needed to compensate for its diversity and heterogeneous quality statistically.

Contextual factors influencing NITAG recommendations, as depicted in Figure 3, are often poorly communicated to the public, who may not understand why one country recommends a vaccine when another does not. Consistent and thoughtful collaboration across stakeholders supports more transparent communications to the public, which can build understanding and trust.

Life course immunisation looks at the broad value of vaccination for individuals, communities, and society across multiple generations. This wider lens requires more data power, collaboration, and multi-disciplinary work at various levels.

With this broader perspective, NITAG recommendations should be designed to achieve clear public health outcomes for all and implemented by governments with clear responsibilities and accountabilities. They should include regular evaluation and adjustments as appropriate based on factors, including vaccine uptake and emerging disease burden.

Population health is an adaptive, dynamic, and unpredictable system with multiple interdependencies and various factors influencing outcomes (16). Analysing data from numerous study designs, RCTs, real-world data, and conceptual models, is required to understand the nuances of health behaviors and interventions and to mitigate the risk of bias or confounding presented by any single data collection methodology. ‘Big data’ analysis involves modern technologies which interpret large volumes of variable data and spot patterns, often in real time (3). This can facilitate effective rapid response and inform long-term planning, as seen during the pandemic when AI was integral to forecasting COVID-19 spread, contact tracing, pharmacovigilance, and fast testing and detection (4).

The applications of AI are vast in public health research and planning. For example, machine learning approaches such as “neural networks” can improve predictive modelling of complex, nonlinear relationships in data. This can support more accurate forecasting of future trends and predicting disease outbreaks based on historical data (17). Natural language processing has been used to analyse vaccine sentiment via social media (18).

Governments and institutions must look at upskilling NITAGs to effectively interpret insights from large volumes of multi-dimensional data, predictive analytics, and conceptual modelling to forecast vaccination needs and outcomes.

Combining multiple data sources presents challenges of standardisation and system interoperability. The European Commission launched the European Health Data Space (EHDS) in May 2022, which will be crucial in harmonising data from across Europe, ensuring data quality, compatibility and security. It is a vital pillar of a strong European Health Union and is the first specific data space to emerge from the European data strategy (19).

Gathering and utilising health data depends on overcoming technical, legal, and implementation challenges to ensure the effective transfer of AI models across different healthcare systems. Data privacy and security are significant hurdles to overcome in the context of public trust and vaccine acceptance, calling for a delicate balance between data access and privacy protection.

EHDS will provide a solid legal framework for using health data for research, innovation, public health, policy-making and regulatory purposes. Under strict conditions, researchers, innovators, public institutions, and industries will have access to high-quality health data crucial to developing vaccines. The availability of large-scale health data can support the generation of robust evidence on vaccine effectiveness and safety. Researchers can analyse data across different populations, age groups, and geographical regions to assess the real-world impact of vaccines, identify potential subgroups that may benefit most from vaccination, and detect rare adverse events. Also, EHDS will facilitate information exchange between Member States on vaccination plans and verification of vaccination certificates.

Collaboration and technology can support access to timely and accurate data during the early phase of an outbreak when the chance for containment is highest (20). Global.health is an open-source platform working towards this by facilitating access to real-time, anonymised health data on infectious disease outbreaks. The platform has a 100-day Mission: to provide decision-makers, researchers, and the public with timely and accurate data during the early phase of an outbreak when the chance for containment is highest. With over 100 million verified case records from 130+ countries, it is a comprehensive repository of COVID-19 line-list data. Facilitating the secure and responsible sharing of health data across European countries can contribute to a deeper understanding of vaccine effectiveness, safety profiles, and real-world outcomes.

Bi-directional communication and collaboration on critical data are required for development, and monitoring and evaluation should be enhanced between governments, NITAGs, and Ministries of Health. There are foundations to build on; for example, the WHO sets research and development targets for funders and developers through target product profiles (TPP), which outline the desired ‘profile’ or characteristics of a target product aimed at a particular disease. TPPs state intended use, target populations and other desired attributes of products, including safety and efficacy-related characteristics (21). Such structures and frameworks with strict data ownership and security protocols support a more coordinated approach to improving vaccine impact through broader coverage and strategic use of certain vaccines.

The risk of communicating inaccurately is significant. When COVID emerged, reporting journalists unintendedly propagated misunderstanding, which fuelled distrust. For example, the media reported daily disease incidences. However, few countries calculated and communicated scientifically valid incidences with a denominator (persons-tested) that reflected the variation in people getting tested daily based on the ever-changing testing recommendations. Media coverage also focused on the COVID-19 vaccine reducing transmission, which to date is almost impossible for respiratory virus vaccines. These can only “control” respiratory tract infection, i.e., minimise morbidity and mortality (22). Understanding and educating the public and working with key stakeholders, including community leaders, to share trusted, accurate information can inform and empower the public.

Governments might look to their NITAGs, with their expertise and multi-disciplinary composition, to help bridge gaps between various stakeholders, promote transparency, and encourage open dialogue.