"Empowering LMICs with AMR Intelligence: Open-Source data science training kit for scalable implementation_Data challenge"

Antimicrobial resistance (AMR) is a growing global health crisis, complicating treatment and increasing healthcare burdens, with the implementation gap between data and action remaining a challenge. In our data challenge project, we propose building an AMR Intelligence through an Open-Source Data Science training kit strategy that will leverage on pathogen and region-specific datasets (e.g., SOAR, SIDERO-WT, ATLAS) available in the Vivli and Wellcome Trust databases to:
Develop hands-on R/Python tutorials, Data storytelling modules, and Interactive dashboards. These will guide users through identifying AMR trends, quantifying resistance rates, visualizing burden by region and pathogen, and simulating stewardship impact. Each module will be localized for example, “Ten-Year AMR Trends in East Africa” to make analysis relevant and actionable for LMIC health systems.
We will propose embedding the modules in the facility-wide electronic medical record (EMR) systems to prompt healthcare workers towards strengthening the judicious use of antimicrobials.
Design a community-driven platform enabling regional customization, where contributors can adapt modules to local contexts, languages, or use cases (e.g., hospital audit, national action plan support).
Strengthening implementation pathways by applying frameworks such as RE-AIM (Reach, Effectiveness, Adoption, Implementation and Maintenance) to ensure adoption, effectiveness, and long-term sustainability.
The project aligns with Vivli’s open science goals by maximizing reach, enhancing data discoverability, and promoting real-world application and by embedding AMR data literacy and stewardship intelligence into existing health education ecosystems. Our initiative will equip Low and middle-income countries (LMIC) stakeholders with open-source tools for actionable intelligence and evidence-based interventions to better understand, predict, and respond to antimicrobial resistance. Ultimately, this project bridges a critical implementation gap turning open data into open knowledge, and open knowledge into action which is scalable.