chisomo-ngoleka/AfriRiskAI

Explainable AI platform for integrating genomic, environmental and demographic data to predict disease risk in African populations. AfriRiskAI AfriRiskAI is an early-stage research and artificial intelligence project exploring how data science, artificial intelligence, and machine learning can support risk identification, assessment, and evidence-based decision-making in African contexts. The project is intended to investigate data-driven approaches for understanding risks affecting communities, institutions, livelihoods, infrastructure, and development. «Project Status: Early-stage research and prototype development. Features described as proposed or planned are not yet operational unless explicitly marked as implemented and tested.» Project Overview African countries face a wide range of interconnected risks, including climate-related hazards, natural disasters, economic shocks, food insecurity, infrastructure vulnerability, and other threats to sustainable development. At the same time, relevant data is often fragmented across different sources, available at different geographic and temporal scales, or difficult for non-technical stakeholders to interpret. AfriRiskAI explores how data integration, statistical analysis, artificial intelligence, and machine learning can help transform available data into useful risk-related insights. The project will initially focus on developing a transparent and reproducible data-science workflow before expanding toward a functional prototype. Problem Statement Many African communities and institutions are exposed to multiple risks, but decision-makers may not always have access to timely, locally relevant, and understandable information about where risks are concentrated and which factors contribute to vulnerability. Traditional risk assessment approaches can also require significant amounts of data, technical expertise, and resources. There is therefore an opportunity to investigate whether data-driven and artificial intelligence approaches can complement existing risk assessment methods by identifying patterns, estimating risk indicators, and s …