Predicting Climate-Driven Antimicrobial Resistance Outbreaks in the Sahel Using AI and Distributed Systems

Antimicrobial resistance (AMR) is a growing public health threat in the Sahel region, exacerbated by climate variability, limited surveillance systems, and fragmented data sources. This work proposes an AI-driven predictive framework that integrates climate data, antimicrobial usage patterns, and distributed computing to forecast seasonal AMR outbreaks in low-resource settings. The system leverages federated learning and edge AI to process sparse and distributed datasets while preserving data privacy. Climate variables from open sources are combined with local health data. Long Short-Term Memory (LSTM) neural networks are used to model temporal patterns and predict AMR peaks, with preliminary simulations achieving 75–85% accuracy in data-sparse environments. The architecture is designed for offline and low-bandwidth conditions common in the Sahel, enabling real-time alerts via SMS/USSD for community health workers. By applying a One Health approach, the model considers human, animal, and environmental factors driving resistance. This research contributes to digital health innovation and epidemic preparedness by demonstrating how artificial intelligence and distributed systems can strengthen AMR surveillance and support community-level health security in resource-constrained African settings.