Developing an Evaluation Framework for Infectious Disease Modelling-to-Policy Pathways: A Qualitative Study Across Five Continents

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ABSTRACT Infectious disease modelling has become an increasingly prominent tool in public health decision-making, with its use accelerating markedly during the COVID-19 pandemic. This growth calls for an understanding of how modelling evidence is received, interpreted, and used – or not used – by decision-makers. There is a recognised need for evaluations of modelling-to-policy systems to understand how to best integrate modelling evidence into decision-making. So far, no comprehensive evaluation framework exists that maps modelling-to-policy pathways. This study addresses that gap by developing a theory of change for modelling-to-policy systems that could serve as a foundation for future evaluations. A qualitative study design was employed, comprising semi-structured interviews with 35 modellers, knowledge brokers, and decision-makers across five continents and diverse institutional settings, spanning high-income and low- and middle-income countries, as well as national and international modelling-to-policy contexts. Thematic analysis was combined with a backward-mapping-informed approach to develop a multi-level evaluative framework. The protocol for this study has been published on March 20, 2025, at OSF ( https://doi.org/10.17605/OSF.IO/J9QXV ). Participants diverged in their conceptualisations of successful modelling evidence use – ranging from instrumental use to accurate understanding and consideration of modelling outputs – yet converged on shared risks: decisions informed by inadequately specified models or by evidence that is misinterpreted due to communication failures. The resulting three-level framework identifies factors directly influencing modelling evidence use across three domains (policy relevance, model quality, and communication and interaction), traces these to enabling conditions, and maps them to systemic enablers – including local and embedded modelling capacity, data infrastructure, knowledge brokering capacity, formal knowledge translation structures, established networks, and funding. The proposed framework represents a first theory of change for modelling-to-policy systems. While it requires further testing and application across diverse decision-making contexts, it offers a structured basis for evaluating existing systems and informing the design of new ones.

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