Policy brief: Improving national vaccination decision-making through data

Front Public Health. 2024 Dec 17:12:1407841. doi: 10.3389/fpubh.2024.1407841. eCollection 2024.

Abstract

Life course immunisation looks at the broad value of vaccination across multiple generations, calling for more data power, collaboration, and multi-disciplinary work. Rapid strides in artificial intelligence, such as machine learning and natural language processing, can enhance data analysis, conceptual modelling, and real-time surveillance. The GRADE process is a valuable tool in informing public health decisions. It must be enhanced by real-world data which can span and capture immediate needs in diverse populations and vaccination administration scenarios. Analysis of data from multiple study designs is required to understand the nuances of health behaviors and interventions, address gaps, and mitigate the risk of bias or confounding presented by any single data collection methodology. Secure and responsible health data sharing across European countries can contribute to a deeper understanding of vaccines.

Keywords: AI technologies; National Immunisation Programs; National Immunisation Technical Advisory Groups; big data analysis; life course immunisation; vaccine policy; vaccine-preventable diseases.

MeSH terms

  • Decision Making*
  • Europe
  • Health Policy
  • Humans
  • Public Health
  • Vaccination* / statistics & numerical data

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. SE received fees from CLCI to coordinate research and publication. CLCI is a registered charity in Belgium and the UK that receives funds from multiple sponsors, none of which contributed to this research. Sponsors list: https://www.cl-ci.org/about/.