Title: AI-driven prediction and surveillance of antimicrobial resistance using integrated multi-source data: A One Health approach
Abstract:
Antimicrobial resistance (AMR) is no longer a looming threat — it is an active crisis. The World Health Organization estimates that drug-resistant infections already claim over 1.27 million lives annually, with projections suggesting the toll could reach 10 million per year by 2050 if left unaddressed. What makes this crisis particularly intractable is not just the biology of resistance but the structure of how we monitor it. Surveillance systems remain fragmented, nationally siloed, and largely limited to clinical settings, leaving the animal and environmental reservoirs of resistance practically invisible to policymakers and public health agencies.
The One Health framework addresses this directly. By treating human, animal, and environmental health as interdependent rather than parallel domains, One Health surveillance forces a reckoning with the full ecological context of AMR emergence and spread. Resistant genes do not respect taxonomic or geographic boundaries; they move through agricultural runoff, livestock operations, hospital wastewater, and community transmission with indiscriminate efficiency. Capturing this movement requires data that is equally indiscriminate in scope.
This study presents a conceptual framework for integrating multi-source AMR data using machine learning and data analytics to enable predictive surveillance at scale. Public datasets — including WHO GLASS, NCBI Pathogen Detection, and environmental monitoring repositories — serve as the input layer. Data preprocessing pipelines standardize resistance profiles, geographic metadata, and temporal indicators across human clinical isolates, veterinary isolates, and environmental samples. Graph-based integration models are then applied to identify cross-domain transmission events, while gradient boosting and spatial interpolation algorithms generate resistance trend forecasts at district, national, and regional levels.
Expected outcomes include identification of high-burden AMR hotspots currently missed by single-domain surveillance, early detection of novel resistance gene circulation before clinical emergence, and predictive risk scores for resistance propagation linked to land use patterns and antibiotic consumption data. These outputs are designed to interface directly with public health policy tools, supporting targeted antibiotic stewardship programs, veterinary regulation reform, and environmental intervention priorities.
The broader implication is straightforward: fragmented surveillance is not just inefficient — it is dangerous. An integrated, AI-driven One Health surveillance system can close the gap between where resistance actually spreads and where public health infrastructure currently looks. As resistance patterns grow more complex and geographically diffuse, computational approaches capable of synthesizing heterogeneous multi-domain data are not a supplement to traditional epidemiology; they are becoming its necessary infrastructure.
