Antimicrobial Resistance Surveillance

Antimicrobial resistance (AMR) surveillance is the systematic collection, analysis, and interpretation of data on resistant microorganisms, antimicrobial use, and related outcomes. It supports clinical decision-making, infection prevention, stewardship, public-health response, and policy evaluation. Effective surveillance increasingly combines laboratory results, prescribing information, patient outcomes, genomic data, and environmental or veterinary observations under a One Health framework.

The surveillance landscape is shifting from fragmented, retrospective reporting toward integrated and more timely systems. Electronic health records, laboratory information systems, standardized susceptibility testing, genomic sequencing, wastewater monitoring, and interoperable reporting can improve detection and traceability. The strongest programs pair technical modernization with consistent case definitions, quality assurance, workforce training, data governance, and mechanisms that translate findings into prescribing and infection-control action.

Artificial intelligence can strengthen AMR surveillance by identifying patterns across microbiology, prescribing, clinical, genomic, and epidemiological datasets. Potential applications include anomaly detection, resistance-trend classification, outbreak signal generation, data deduplication, and decision support. Its value depends on representative training data, transparent validation, human oversight, cybersecurity, and safeguards against automation bias. AI should augment laboratory and public-health expertise rather than replace confirmatory testing or accountable clinical judgment.

North America generally emphasizes interoperable laboratory and clinical reporting, stewardship measurement, and genomic epidemiology. Latin America faces a strong need to expand laboratory networks, harmonize reporting, and improve coverage beyond major urban centers. Europe benefits from coordinated public-health structures and standardized methodologies, while continuing to address cross-border comparability and One Health integration. The Middle East is strengthening laboratory capability and regional coordination amid varied health-system capacity. Africa’s priorities include dependable diagnostics, workforce development, specimen referral networks, and representative community and animal surveillance. Asia-Pacific combines advanced national programs with substantial variation in infrastructure, requiring scalable digital tools, regional data exchange, and stronger rural coverage.

ASEAN cooperation is particularly relevant for cross-border data exchange, laboratory strengthening, and surveillance of travel- and trade-related transmission. BRICS members can advance collaboration on diagnostics, genomic methods, antimicrobial-use monitoring, and capacity building while accounting for diverse health systems. The European Union benefits from coordinated standards and cross-border response mechanisms. G7 members can support methodological alignment, innovation, and sustained assistance for global surveillance. GCC countries have opportunities to coordinate laboratory quality, hospital surveillance, and regional outbreak intelligence. NATO members can reinforce preparedness for health security threats, resilient laboratory networks, and secure information sharing while maintaining civilian public-health priorities.

Australia continues to benefit from coordinated national monitoring while needing sustained attention to remote and Indigenous-community access. Brazil’s priorities include expanding geographically representative laboratory and One Health surveillance. Canada emphasizes linkage across provinces and territories, Indigenous health equity, and antimicrobial-use context. China is strengthening laboratory, hospital, and genomic capabilities across a large and varied system. France, Germany, Italy, and Spain continue to align national reporting with European standards while improving hospital, community, and environmental integration. India’s scale makes diagnostic quality, sentinel-network expansion, and regional comparability central priorities. Japan and South Korea combine advanced laboratory capacity with needs for integrated data and aging-population preparedness. Mexico is focused on broader laboratory coverage and consistent national reporting. Russia requires resilient surveillance connectivity and standardized data practices across its extensive geography. The United Kingdom emphasizes coordinated national monitoring, stewardship, and genomic intelligence. The United States combines extensive laboratory and clinical data assets with ongoing challenges in interoperability, representativeness, and equitable coverage.

Industry and health-system leaders should first define surveillance decisions, reporting responsibilities, and measurable response thresholds. They should then invest in interoperable laboratory and clinical infrastructure, standardized susceptibility methods, external quality assessment, and secure data exchange. Programs should combine human, animal, food, and environmental signals where appropriate; close geographic and population coverage gaps; and create feedback loops linking findings to stewardship and infection prevention. AI initiatives should begin with high-value, auditable use cases and prospective validation. Durable progress also requires workforce development, transparent governance, privacy protection, procurement standards, and collaboration across public-health, clinical, veterinary, academic, and laboratory stakeholders.

This executive summary uses a structured review of authoritative public-health and scientific evidence relevant to AMR surveillance. The assessment prioritizes surveillance standards, national and regional reporting frameworks, peer-reviewed research, laboratory guidance, One Health approaches, and documented digital and genomic practices. Findings are synthesized thematically across technology, governance, clinical implementation, regional systems, country capacity, and responsible AI. Evidence is interpreted cautiously, with attention to methodological comparability, reporting completeness, geographic variation, and the distinction between demonstrated applications and emerging possibilities.

AMR surveillance is most effective when reliable data move quickly from laboratories and care settings to clinicians, infection-prevention teams, policymakers, and communities. The next phase should prioritize interoperability, representative coverage, One Health coordination, genomic and environmental integration, and responsible use of AI. Progress will depend less on isolated tools than on sustained standards, skilled personnel, secure governance, and clear pathways from detection to intervention across regions and national systems.