Data Challenge: PREDICT-AMR

Antimicrobial resistance (AMR) is one of the top 10 global health threats, and antimicrobial-resistant infections are estimated to cause more deaths than ischemic heart disease by 2050. The threat is particularly acute in sub-Saharan Africa, where surveillance systems are underutilized and evidence to inform stewardship critically limited. Our team has expertise in clinical microbiology, infectious disease pharmacology, epidemiology, and data analysis and hands-on experience in development of antibiograms, analysis of resistance patterns in clinical isolates and generation of evidence for antimicrobial stewardship programs in low resource settings. We share a common vision to translate surveillance data into public health policy action across Africa. This project tackles the urgent needs to inform public health practice and strengthen health systems through the development of predictive models of the trajectories of evolving resistance across 3 core objectives. We will standardize and extract target bacterial isolates, patient demographics (age and gender), and clinical specimen types (e.g. blood, urine) using longitudinal minimum inhibitory concentration (MIC) data from the Pfizer ATLAS program and regional, hospital-level data from the GSK SPIDAAR study. Finally, this study bridges the gap between on-the-ground surveillance and high-level macro policy. Our results will provide actionable evidence to help optimize antibiotic stewardship, direct strategic health system resources, and guide future countermeasures to the most critical public health vulnerabilities by mapping where future clinical resistance will be faster than active drug development. I think this challenge is a great opportunity to generate evidence that directly addresses the AMR burden in underserved populations. ” We are committed to open science, collaborative learning, and sharing our work that can meaningfully inform stewardship programs and health policy across Africa and beyond.