Code sharing and artificial intelligence can help decolonise public health modelling

Steps towards democratisation of efforts in global health and capacity strengthening in low and middle income countries (LMICs) have made significant progress in recent years.1234 These efforts have empowered local researchers with the skills to collect, process, and analyse data, bridging the data generation gap between the global north and south. But glaring disparities remain in the realm of modelling and prediction studies, which are predominantly led by countries in the global north, often with limited transparency in sharing the underlying codes.5 This absence of mandates to share codes when publishing in journals exacerbates the challenge for researchers from LMICs, who are often located in the global south. Artificial intelligence (AI) and ethical code sharing practices can narrow this divide, empowering researchers in resource constrained settings to benefit from public health modelling.Public health modelling for predictive or analytical purposes directly influences public health policy and decision making in LMICs—this makes…
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