Toward Actionable and Equitable AMR Forecasting: A Standards-First Blueprint Linking Clinical and Environmental Resistomes.

Antimicrobial resistance (AMR) forecasting-here defined as the use of statistical or machine-learning models to anticipate future resistance trajectories, patterns, and emerging hotspots-has progressed rapidly but remains fragmented by heterogeneous standards, limited cross-sector integration, and insufficient ethical and equity safeguards. Building upon recent contributions to the Indian Journal of Microbiology, we present a standards-first blueprint structured around four pillars: (i) a minimal common data element (mCDE) backbone for harmonized AMR data architecture; (ii) systematic integration of environmental and food-chain resistomes to support One Health prediction models; (iii) a five-level Forecast-to-Action Readiness Level (FRL) framework to link forecasts with real-world stewardship and public-health interventions; and (iv) explicit ethics, transparency, equity, and capacity-building requirements to ensure fair participation of low- and middle-income countries (LMICs). The blueprint emphasizes reproducibility, environmental complementarity, and decision-aware forecasting, with alignment to WHO GLASS and regional AMR programs. This structured protocol offers a scalable and implementation-ready pathway toward equitable, cross-sector AMR forecasting networks.