Home > Guidance for using modelling for immunization decision-making

This new guidance from the World Health Organization (WHO) provides practical support for using mathematical modelling to inform immunization policy and programme decisions. Developed as a joint initiative between WHO and the Immunization and Vaccines-related Implementation Research Advisory Committee (IVIR-AC), the guidance aims to bridge the gap between modelling outputs and real-world policy needs. It is designed as a first-line resource for stakeholders involved in immunization decision-making—including NITAG and RITAG members, EPI managers, and technical partners—supporting them in understanding, interpreting, and applying modelling evidence. The development of this guidance is informed by a qualitative needs assessment of immunization decision-makers and modelers across WHO regions. A related commentary can be read on Vaccine. 

Key features

  •  Introduces core concepts of mathematical modelling and their role in immunization decision-making
  •  Links modelling outputs to the Evidence-to-Decision framework used by advisory bodies
  •  Provides guidance on engaging effectively with modellers, including roles and collaboration approaches
  •  Outlines how to interpret, evaluate, and critically appraise model results, including uncertainty and assumptions
  • Includes practical tools and annexes to support model development, validation, and scenario analysis

How can this resource be used?

This guidance can help:

  • Strengthen the capacity of NITAGs, RITAGs, and EPI managers to use modelling evidence
  • Support more transparent and informed decision-making
  • Improve collaboration between policy-makers and modelling teams
  • Ensure modelling results are relevant, credible, and applicable to local contexts

How can the findings be used?

Mathematical models are increasingly used to inform decisions on vaccine introduction, delivery strategies, and programme optimization, helping decision-makers explore “what if” scenarios when empirical data are limited. However, translating modelling results into policy remains a challenge. This guidance addresses that gap by supporting countries to better engage with modelling processes and make informed, context-specific decisions.

Thumbnail image credit: WHO 

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