Bridging the Gap: Enhancing the Evaluation & Interpretation of Epidemic Forecasts for Researchers & Policymakers in Resource-Constrained Settings

The COVID-19 pandemic highlighted the challenges of generating, interpreting, and acting on evidence in fast-moving, politically charged contexts. Epidemic forecasts became central to navigating uncertainty, yet their use, interpretation, and communication varied widely across settings. We conducted a global mixed-methods study, combining an online survey with 143 participants from 46 countries and 13 semi-structured interviews with individuals involved in COVID-19 policy dialogues. Forecasts informed a range of policy questions, from estimating epidemic size and health system needs to planning interventions, but their perceived value depended on clarity, contextual relevance, and timeliness. Decision-makers in high-income settings often used forecasts to explore scenarios and quantify uncertainty, supported by stronger modelling capacity, while counterparts in low- and middle-income countries emphasized the role of expert briefings and locally tailored insights in the face of limited baseline data and modelling infrastructure. Across contexts, forecasts were most actionable when they informed binary choices or compared concrete scenarios. Barriers to uptake included late delivery, lack of contextual fit, and limited technical familiarity, especially in resource-constrained settings. Strengthening forecast impact in such contexts will require modular, user-oriented tools, embedding modellers in response teams, co-developing decision-relevant metrics, and investing in foundational health data systems. These strategies can help ensure that forecasting is both technically robust and operationally relevant for future public health emergencies.