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<p>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 <i>Indian Journal of Microbiology</i>, 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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • M. Vijayasimha,
  • S. Sreedevi

摘要

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.