The COVID-19 pandemic highlighted the essential role of disease modeling in shaping public health responses. However, models designed in high-resource settings often fail to capture disease dynamics accurately in lower-resource contexts like Ghana, where socio-ecological factors, infrastructure constraints, and data fragmentation complicate accurate predictions. In this Commentary, we examine the challenges of adapting global modeling approaches to Ghana’s context and propose strategies to improve their accuracy, relevance, and policy utility. These challenges were further compounded during the pandemic recovery period, when Ghana simultaneously faced outbreaks of Marburg virus and Mpox. These additional pressures—against a backdrop of rapid urbanization, increased human-wildlife interaction, shifting transmission dynamics, and environmental degradation—underscore the limitations of current modeling approaches. A key limitation lies in the difficulty of collecting raw, disaggregated data, accounting for sociocultural determinants, and capturing the complex interplay between disease dynamics and adaptive behaviors. Addressing these challenges requires valid, timely, and disaggregated data on social and epidemiological dynamics for model parameterization and validation. To examine the challenges faced in adapting global models for local use, we focus on Ghana’s unique context and argue for a rethinking of modeling approaches in this commentary. To mitigate potential harm, it is imperative to emphasize context-specific data, interdisciplinary input, and integration of social and economic factors, as foundational principles for future frameworks that can better support pandemic preparedness in Ghana and similar settings.
