Identifying drivers of antibiotic resistance: Mathematical model of One Health interventions spotlights transmission and environmental pathways

Antimicrobial resistance (AMR) is a global public health emergency. One Health interventions—an integrated approach recognizing AMR as driven by reciprocal interactions between humans, animals, and environment—are key to addressing this threat, but, without understanding relative contributions of selection and transmission in different settings, it is impossible to improve intervention targeting. While existing analysis focuses on data from one setting, modeling provides an opportunity to integrate multiple data sources and explore intervention impact. Here, we use a deterministic compartmental model calibrated to third-generation cephalosporin-resistant Escherichia coli in three countries (Denmark, England, and Senegal) to estimate the impact of intervention packages. We find that the biggest impact came from targeting bacterial spread and environment. We also show that there is, however, an urgent need to improve AMR surveillance: our model could only recreate AMR dynamics with high uncertainty due to lack of sufficient data across sectors. Without such data, it remains impossible to effectively prioritize interventions.