Haryini S/Vivli AMR data challenge-2025

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: Sree Haryini, Shraddha Madhav Karve, George Priya Doss C
DOI: 10.5281/zenodo.19480850
Cited by: 1

Abstract

In this study, we leverage AMR surveillance data for the fungal pathogen Candida glabrata to demonstrate the value of large-scale spatiotemporal data in studying and predicting resistance patterns. Using Pfizer’s SENTRY dataset from the United States and Europe, we examine the impact of air pollution on resistance to fluconazole in C. glabrata. Notably, our time-series analysis reveals that time-lag effects play a significant role in predicting resistance trends.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 April 2026
Language
EN
Document type
Dataset