Data Challenge: Using Artificial Intelligence to predict antimicrobial resistance data disparities across Africa

This study investigates the landscape of antimicrobial resistance (AMR) surveillance data across Africa by integrating and analyzing datasets from both industry and public sources. AMR poses a significant global health threat, particularly in Africa, where surveillance and data collection are often fragmented and inconsistent. The industry datasets utilized are Pfizer's (ATLAS), Johnson & Johnson's (DREAM). Public datasets are sourced from The Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP), which provides a comprehensive overview of AMR trends across various African countries. The study has two primary objectives: First, it aims to compare the distribution and comprehensiveness of AMR data between industry and public sources across different African countries. By doing so, it seeks to identify disparities in data coverage, quality, and accessibility. This comparative analysis will highlight gaps in current surveillance efforts and emphasize the need for more cohesive and coordinated data collection strategies. Second, the study employs advanced artificial intelligence (AI) techniques to predict the distribution of AMR across the continent. Machine learning models are trained on the integrated datasets to identify patterns and trends in AMR prevalence. These predictive models will provide insights into potential future hotspots of antimicrobial resistance, allowing for more targeted and effective interventions. The use of AI in this context not only enhances the accuracy of predictions but also facilitates the analysis of large, complex datasets that would be challenging to process using traditional methods. Ultimately, this study aims to contribute to the global effort to combat AMR by providing a clearer understanding of its distribution in Africa and demonstrating the potential of AI-driven approaches in enhancing AMR surveillance and prediction. Through a comprehensive analysis of both industry and public data sources, this research underscores the importance of collaborative efforts in addressing the growing threat of antimicrobial resistance.