Finalist
Team 13302: Robin Dosan (Team lead, Oxford University Clinical Research Unit (OUCRU) Indonesia), Mesyalie Lorenza (Summit Institute for Development), and Anuraj Shankar (Oxford University Clinical Research Unit (OUCRU) Indonesia)
Antimicrobial resistance (AMR) is a critical global health threat, projected to cause up to 10 million deaths annually by 2050 if left unaddressed. While genomic approaches have advanced pathogen DNA mapping, genotypic data remains underutilized for actionable surveillance and public health responses. The Pfizer Antimicrobial Testing Leadership and Surveillance (ATLAS) dataset offers a complete resource to bridge this gap with the pairing of phenotypic minimum inhibitory concentration (MIC) data with genotype findings from 83 countries.
We propose to leverage the genotypic data from this dataset using federated analytical framework, which will allow us to outline region-specific resistance profiles while ensuring sensitive data remains secure and decentralized.
Our approach applies federated genomics analysis to stratify the genotype data by countries and time period. First, we will map the geographic and temporal distribution of key resistance genes, such as carbapenemase and CTX-M variants across blood infection-related Enterobacterales. Second, we will develop and simulate federated framework that link genotypic resistance to MIC to enable local prediction of resistance patterns without sharing isolate genotype data across jurisdictions. Ultimately, we want to develop an open-source tools and dashboard that visualizes region-specific genotype–phenotype linkage to support decision-making.
We believe that this work will directly address multiple AMR priorities. It will help public health stakeholders by providing a scalable and secure surveillance model for their own genomic data. Moreover, it will enhance health systems by demonstrating how federated approaches can unlock the value of decentralized surveillance where data-sharing is limited by regulation (GDPR, HIPAA, or local regulation). In the end, identifying emerging resistance genes and applying targeted interventions will lead to better regional AMR stewardship and patient outcomes.