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At least 19 recordsLinked to original sources
PubMed2025

Oropouche Virus Importation in Southern Brazil and Emerging Concern Calling for Enhanced Public Health Surveillance.

Oropouche virus (OROV), an arthropod-borne virus transmitted by Culicoides paraensis, is an endemic arbovirus that historically circulates mostly in the Amazon basin. Between 2022 and 2024, it reemerged as a more widespread public health concern in South America. We conducted a pooled-sample molecular surveillance study to understand the prevalence of Oropouche fever in Brazil's southernmost state. Over 18 months, we analyzed 4060 samples to monitor the virus emergence in the Rio Grande do Sul state. We detected the first human case of OROV in the state, and our phylogenetic reconstruction indicated a travel-related introduction from the Amazon region into Rio Grande do Sul. Despite the absence of local transmission, the invasion of Culicoides paraensis and enzootic circulation of the OROV in Rio Grande do Sul highlight the risk of Oropouche fever outbreaks in the region. We demonstrated that pooled-sample surveillance effectively monitors virus introduction during periods of low endemic circulation, serving as an essential active surveillance tool for the timely detection of virus emergence and enhancing public health preparedness. The multiple introductions of distinct OROV lineages into southern Brazil underscore the importance of genomic surveillance and public health strategies to monitor and mitigate arbovirus spread in the region.

Brazil↗
PubMed2026

Mapping High-Rate Clusters of Animal Contact-Related Human Salmonella enterica Single-State Outbreaks in the United States, 2009-2022: A Spatial Epidemiological Approach to Inform Public Health Surveillance.

INTRODUCTION: Nontyphoidal Salmonella enterica (NTS) is a major zoonotic enteric pathogen. Animal contact-related NTS outbreaks have increased in the United States over the last decade. Geospatial analysis can identify locations with elevated risk of NTS outbreaks where public health authorities can focus their NTS prevention and intervention efforts. METHODS: We analysed NTS outbreak data reported from individual states to the Centers for Disease Control via the National Outbreak Reporting System between 2009 and 2022 across the continental contiguous United States. A geospatial analytical framework that included disease mapping, spatial interpolation, and global and local clustering methods was applied to identify regions with high NTS outbreak rates. Given that the study period (2009-2022) included the COVID-19 pandemic, an interrupted time series negative binomial model was used to assess changes in NTS incidence before and after 2020. RESULTS: A total of 104 NTS single-state outbreaks were reported to the National Outbreak Reporting System (NORS) during the study period. The mean annual incidence rate was 0.02 NTS outbreaks per million person-years. The primary animal contact categories associated with outbreaks were mammals (cattle, pigs, sheep, and horses), birds (backyard chickens, ducklings, and turkeys), and reptiles (turtles and lizards). Exposure settings included farms, fairgrounds, agricultural feed stores, veterinary clinics, dairy/agricultural settings, and residential settings. The local cluster detection methods consistently identified areas with significantly high NTS animal contact-related outbreak rates in the Mountain West, Midwest, and Northeast of the US. The interrupted time series analysis indicated a reduction in incidence following the onset of the COVID-19 pandemic (IRR = 0.03; p = 0.06). CONCLUSION: NTS animal contact-related single-state outbreaks revealed distinct spatial clustering across the United States, with higher risks in the Mountain West, Midwest, and Northeast. Diversity of animal-contact sources and exposure settings depicted complex transmission dynamics of NTS. A decline in reported NTS outbreaks was observed after the COVID-19 pandemic. Focused prevention and control programs are needed in high-risk areas to mitigate the burden of NTS outbreaks.

United States↗
PubMed2026

Toward a unified approach: Considerations for bioinformatic and sequencing activities & data in wastewater surveillance of biologic public health threats.

Genomic technologies such as PCR and next-generation sequencing (NGS) have greatly advanced public health surveillance, especially during COVID-19, by enabling detailed tracking of pathogen spread, origins, and variants. While PCR is vital for targeted detection, falling NGS costs have made large-scale, high-throughput sequencing more feasible, supporting broader pathogen monitoring-including the detection of vaccine escape variants and new strains. Applying NGS to wastewater offers valuable population-level insights but faces challenges such as variable sample complexity, the need for skilled staff, suitable platforms, and robust IT infrastructure. Although there are currently a lot of efforts towards defining guidelines for sampling, analysis, and integrating wastewater data into public health policy, such as the recently published International Cookbook for Wastewater Practitioners, they often lack universal applicability, emphasizing the analytical approaches in favour of the NGS-based approaches. However, standardising protocols for sampling, sequencing, and analysis is crucial to ensure reliable, comparable data across surveillance systems worldwide. Pilot studies and continuous refinement are recommended to overcome implementation hurdles and fully realise the benefits of NGS in wastewater surveillance. This work attempts to outline these challenges and opportunities across the entire wastewater surveillance workflow, from data generation to reporting, and provide some concrete suggestions and considerations across the spectrum of activities. We further highlight that the infrastructure, funding and government-policy context in which surveillance operates acts as an enabling condition for these activities, and that technical standardisation alone is unlikely to deliver durable, comparable surveillance in its absence.

considerations↗
PubMed2026

Public Health Indoor Air Surveillance for Respiratory Pathogens: From Pilot to Citywide Implementation.

CONTEXT: Environmental surveillance has become an essential component of public health pathogen surveillance programs. Indoor air surveillance is a promising environmental surveillance method but has yet to be scaled citywide and incorporated into state and local public health programs. PROGRAM: The Chicago Department of Public Health established a citywide indoor air surveillance program to enhance monitoring of airborne pathogens and address gaps in existing surveillance. IMPLEMENTATION: The program began with a pilot phase from February to April 2023 at 5 sites, which informed expansion to 17 sites and 20 samplers across emergency departments (5), congregate (3), and community settings (15), across the city. Site staff conducted weekly cartridge exchanges for seven-day sample collection periods using AerosolSense and AirPrep Cub samplers, which were then processed at the Regional Innovative Public Health Laboratory for SARS-CoV-2, influenza, and respiratory syncytial virus. Samples were tested using quantitative polymerase chain reaction, and SARS-CoV-2-positive samples underwent whole genome sequencing to characterize circulating viral lineages. EVALUATION: From February 2023 to August 2025, 1246 samples were processed, with a mean compliance of 85% (SD = 0.149) for weekly cartridge exchanges and minimal operational disruption. The program data supported its use as a surveillance tool for respiratory pathogen detection and SARS-CoV-2 lineage monitoring, with 74% samples positive for at least 1 virus and 68% detecting SARS-CoV-2. DISCUSSION: The program successfully scaled to citywide coverage and was shown to be feasible and acceptable across sites. These results highlight the value of indoor air monitoring as a complementary surveillance tool and offer a framework for other jurisdictions seeking to enhance respiratory pathogen detection through establishing a citywide indoor air surveillance program. Facility-level sampling is aggregated across sites to capture citywide trends complementing clinical and wastewater surveillance, and provides insights into facility-level pathogen burden, not captured by other surveillance methods.

Humans↗
PubMed2026

Project ODIN: advancing environmental genomic surveillance for public health across sub-Saharan Africa.

Persistent SARS-CoV-2 transmission, ongoing mpox outbreaks, and the continued spread of endemic diseases such as typhoid fever and cholera underscore the urgent need for global, multiomics surveillance. In this Personal View, we present Project ODIN, a consortium of European and African partners launched in 2023 that aims to meet this challenge by deploying innovative systems for near real-time pathogen detection and actionable public health insights. The project is a collaboration between high-income and low-income countries in northern Europe and sub-Saharan Africa. Focusing on low-income and middle-income countries, ODIN integrates metagenomics with mobile laboratory systems for comprehensive pathogen monitoring across diverse environments. ODIN emphasises standardised sampling, bioinformatics pipelines, and data-sharing protocols to ensure reliable, interoperable results while addressing infrastructure and resource limitations. By bridging gaps in genomic surveillance, these initiatives seek to strengthen outbreak preparedness, improve pathogen detection, monitor antimicrobial resistance, and provide a holistic approach to One Health challenges. Together, these innovations could advance global surveillance capacity-particularly in under-resourced regions-paving the way for effective disease control and evidence-based policy making.

Humans↗
PubMed2026

Mapping Wastewater Pathogens and Their Associated Environmental and Public Health Risk Factors: A Systematic Review and Meta-Analysis.

BACKGROUND: Wastewater-based epidemiology (WBE) has emerged as a critical tool for public health surveillance, yet its application across diverse pathogens and geographical settings remains inconsistent. This systematic review synthesizes global evidence on wastewater surveillance to identify associated risk factors. METHODS: Following PRISMA 2020 guidelines (PROSPERO: CRD420261297382), a systematic search was conducted across PubMed, Scopus, Google Scholar, and Web of Science for studies published between 2000 and 2025. RESULTS: Thirty-nine peer-reviewed studies were included. The evidence base is geographically skewed toward the European Region (48.7%) and the Americas (23.1%), with significant underrepresentation in LMICs. Viruses were the primary biological target (89.7%), followed by bacteria (7.7%) and parasites (2.6%). A proportion meta-analysis of 31 eligible studies demonstrated a pooled wastewater pathogen detection prevalence of 62% (95% CI: 47.5-74.6%), with the European Region yielding the highest regional estimate (73%) and the African Region the lowest (8.3%). Conventional PCR and sequencing methods showed higher pooled detection rates (92.4% and 90.1%, respectively) than RT-qPCR (47.9%). CONCLUSION: WBE provides a robust early-warning system indicating a need for broader pathogen diversity, incorporating bacterial and parasitic surveillance and expansion into rural and resource-limited regions.

Contamination↗
PubMed2026

National Antimicrobial Resistance Monitoring System: Three Decades of Advancing Public Health Through Integrated Surveillance of Antimicrobial Resistance.

Antimicrobial resistance (AMR) occurs when bacteria and other microorganisms adapt in ways that make medicines less effective, causing infections that are harder to treat and more likely to spread. According to the Centers for Disease Control and Prevention (CDC), AMR infections affect millions of Americans each year and contribute to thousands of deaths (CDC, 2019). After three decades of operation, the U.S. National Antimicrobial Resistance Monitoring System (NARMS) stands as a model of sustained, collaborative public health surveillance. What began in 1996 as an effort to track resistance in Salmonella and E. coli O157 has evolved into a One Health surveillance network monitoring AMR across the farm-to-fork continuum. Through a partnership among CDC, the Food and Drug Administration (FDA), the U.S. Department of Agriculture (USDA), state and local health departments, and universities, NARMS has become the backbone of foodborne AMR surveillance in the United States. The past decade has been particularly transformative. NARMS explored new sampling to include companion animals, minor livestock, aquaculture, surface water, and wildlife. Whole-genome sequencing (WGS) revolutionized the program's capabilities, enabling timely identification of emerging pathogens and revealing how resistance genes spread. Near real-time public dashboards make NARMS data accessible to researchers, clinicians, regulators, and policymakers. NARMS data shape decisions about new animal drug approvals, guide stewardship programs, and inform clinical treatment guidelines nationwide. As NARMS enters its fourth decade with a 2026-2030 strategic plan, the program will leverage artificial intelligence and metagenomics while expanding surveillance to fill remaining gaps ensuring this vital system continues to protect the food supply and both human and animal health from AMR.

Antimicrobial Resistance (AMR)↗
PubMed2026

Correlation assessment of SARS-CoV-2 variants and their subvariants present in clinical and wastewater samples in Oregon, USA (February 7, 2021 - February 26, 2022) using the Freyja bioinformatics approach.

BACKGROUND: Wastewater surveillance is a valuable tool for monitoring SARS-CoV-2 at the community level. As the virus diversified into many variants and subvariants that share overlapping mutations, resolving them accurately from wastewater becomes a key bioinformatic challenge. OBJECTIVES AND AIMS: This study evaluated two distinct bioinformatic approaches, multilocus sequence typing (MLST) and Freyja, for identifying SARS-CoV-2 variants and subvariants in Oregon wastewater samples collected from February 2021 to February 2022. METHODS: The MLST approach identified SARS-CoV-2 variants using unique mutations curated from clinical samples. In contrast, the Freyja approach resolved variant and subvariant abundances using genome wide mutation profiles weighted by sequencing depth. In this study, the variant and subvariants relative abundances produced by both approaches were compared against those observed in clinical surveillance data. RESULTS: Both approaches identified SARS-CoV-2 variants at relative abundances that agreed closely with those observed in clinical surveillance data. However, only the Freyja approach identified over 200 Delta subvariants, divided into three clades (21A, 21I and 21J) and two levels (Level 1 and 2) based on Pango subvariants. Delta subvariants showed strong agreement at Level 1 subvariants (rs = 0.892-0.944), while agreement at Level 2 subvariants was inconsistent (rs = 0.324-0.903). CONCLUSIONS: The Freyja approach provided enhanced resolution of SARS-CoV-2 variants and subvariants in wastewater, at abundances that agreed with clinical surveillance. This added resolution is a critical advantage for public health surveillance as SARS-CoV-2 continues to evolve and share mutations across variants and subvariants.

Oregon↗
PubMed2026

Evaluation of Oxford nanopore sequencing for antimicrobial resistance surveillance in Salmonella: comparison with phenotypic antimicrobial susceptibility in a large-scale study.

UNLABELLED: Salmonella is a major zoonotic foodborne pathogen, and antimicrobial resistance (AMR) in Salmonella presents a significant public health challenge. Compared with conventional antimicrobial susceptibility testing (AST), whole-genome sequencing (WGS) provides a more rapid and comprehensive approach to AMR characterization, thereby informing antimicrobial selection and supporting public health surveillance. In this study, Oxford Nanopore Technology (ONT)-based WGS was performed on 1,490 Salmonella isolates collected through nationwide surveillance in Taiwan in 2025. Genotypic resistance inferred from WGS data was compared with phenotypic AST results to assess the performance of ONT-WGS. Overall, WGS-inferred resistance showed high concordance with phenotypic resistance for most antimicrobials. However, major genotype-phenotype discordance was observed, attributed to four categories: (i) breakpoint-dependent classification, (ii) reduced or absent phenotypic expression of resistance genes, (iii) minimum inhibitory concentration (MIC) modulation by ramAp, and (iv) absence of known AMR determinants. Notable discrepancies included tigecycline resistance without known genetic determinants, nalidixic acid resistance linked to ramAp-mediated MIC elevation, and a high prevalence of colistin resistance (35.7%) in S. Enteritidis, with most resistant isolates lacking identifiable AMR determinants. Additionally, a significant proportion of ESBL- and AmpC-producing isolates were classified as susceptible or intermediate to cefotaxime and ceftazidime under CLSI criteria, highlighting the potential for misclassification and treatment failure. These findings demonstrate that ONT-WGS enables accurate and comprehensive AMR characterization by directly identifying resistance determinants and avoiding potential misclassification associated with breakpoint-based AST interpretations. When interpreted appropriately, WGS can support better antimicrobial selection and serve as a valuable alternative to conventional susceptibility testing. IMPORTANCE: Accurate prediction of antimicrobial resistance is essential for appropriate therapy and effective surveillance of Salmonella. However, discordance between genotype-based predictions and phenotypic antimicrobial susceptibility testing (AST) can complicate clinical interpretation. In this nationwide study of 1,490 Salmonella isolates, we show that Oxford Nanopore Technology-based whole-genome sequencing (ONT-WGS) provides rapid and comprehensive detection of antimicrobial resistance determinants with high concordance to phenotypic AST. We further identify four major mechanisms underlying genotype-phenotype discordance, including breakpoint-dependent classification, reduced or absent phenotypic expression of resistance genes, minimum inhibitory concentration (MIC) modulation by ramAp, and the absence of known AMR determinants. These findings demonstrate how WGS can complement conventional AST, improve interpretation of challenging susceptibility results, and strengthen genomic surveillance of emerging antimicrobial-resistant Salmonella.

Microbial Sensitivity Tests↗
PubMed2026

Core genome and whole genome multi-locus sequence typing of Cronobacter isolates.

UNLABELLED: Cronobacter species, especially C. sakazakii and C. malonaticus, are opportunistic pathogens that are linked to severe infections in infants with high case fatality rates. In this study, we investigated whole genome sequencing (WGS) analysis approaches, specifically 7-gene multi-locus sequence typing (7-gene MLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST) to subtype Cronobacter isolates. We analyzed a comprehensive set of 743 Cronobacter isolates derived from clinical, food, and environmental sources. We also evaluated high-quality single nucleotide polymorphism (hqSNP), cgMLST, and wgMLST to cluster epidemiologically related and differentiate sporadic C. sakazakii isolates. Our results indicate that both cgMLST and wgMLST accurately identify closely related isolates and are consistent with epidemiological findings. The allele-based analyses were also comparable with hqSNP analyses, the current gold standard. Our workflow also outputs 7-gene MLST allele calls, Cronobacter sequence types, and clonal complexes, which may be useful for historic comparisons during outbreak investigations. Following the recent classification of Cronobacter infections as nationally notifiable in the United States, our findings demonstrate the efficacy of WGS-based approaches within the PulseNet framework to improve outbreak detection and response strategies for Cronobacter. IMPORTANCE: Cronobacter species, specifically C. sakazakii and C. malonaticus, are opportunistic pathogens linked to severe infections in infants with high case fatality rates. This study highlights the critical importance of advanced molecular techniques in public health surveillance, using whole genome sequencing (WGS) methodologies such as multi-locus sequence typing (7-gene MLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST). The validation of these WGS-based approaches within the PulseNet framework is timely, especially following the recent classification of Cronobacter infections as nationally notifiable in the United States. WGS methods not only enhance outbreak detection but can also inform public health guidance aimed at preventing infections and reducing mortality in vulnerable populations, especially infants. Our research supports implementation of cgMLST as a standardized approach for routine PulseNet surveillance of Cronobacter, with wgMLST and hqSNP analyses providing additional discriminatory power for outbreak investigations and high resolution phylogenetic analysis.

Multilocus Sequence Typing↗
PubMed2026

Making waves: toward systems-level interpretation of hormonal and endogenous biomarkers in wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has proven invaluable for population health monitoring, most notably during the COVID-19 pandemic. Yet current WBE largely relies on exogenous markers such as drugs, pathogens, and their metabolites, limiting surveillance to what communities are exposed to. We argue for expanding WBE towards endogenous biomarkers, particularly hormones, which provide insights into physiological stress, metabolic function, and endocrine activity. Hormone-based WBE offers new opportunities to capture population-level biological responses to societal and environmental stressors, disasters, and chronic disease burdens at the community scale. This perspective outlines a systems-level framework for integrating hormonal signals in wastewater with clinical data, behavioral indicators, environmental factors, and digital markers to support more robust and context-aware public health surveillance. We highlight key technical considerations, interpretive challenges, and opportunities for translational pilot studies. By moving beyond exposure tracking toward more integrated interpretation of biological responses, hormone-informed WBE may contribute to more resilient, inclusive, and actionable public health infrastructure.

Humans↗
PubMed2026

Ethical Governance of Open Data Across Biomedical Research, Healthcare, and Public Health: Privacy, Equity, Trust, and Controlled Access.

Open data has become central to biomedical research and public health, but health information is uniquely sensitive and difficult to share responsibly. In this narrative review, open data is considered as a spectrum of health-data sharing arrangements, ranging from public aggregate datasets to controlled-access repositories, federated analysis, and synthetic data. This narrative review synthesizes the scientific and societal rationale for greater openness with the ethical, legal, and governance constraints that shape what "open" can realistically mean in healthcare. We examine how data sharing supports reproducibility, machine learning, and more efficient research, while also enabling public health surveillance and learning health systems. Against these benefits, we analyze privacy and re-identification risks, consent challenges in large-scale secondary use, inequities including data colonialism, and tensions introduced by commercialization. We integrate lessons from prominent case examples spanning pandemic data sharing, genomic initiatives, population registries, patient-led rare disease infrastructures, and regional data spaces. Across these domains, experience suggests that durable progress depends less on unrestricted openness than on calibrated access, privacy-preserving architectures, clear accountability, and sustained public engagement. We conclude by proposing a pragmatic ethical orientation for healthcare open data: treat openness as a spectrum of controlled sharing arrangements, embed equity and reciprocity into governance, and institutionalize trust-building measures that can persist beyond emergencies and political cycles.

Data colonialism↗
PubMed2026

Dynamic case-control sampling for rapid estimation of vaccine effectiveness against an emerging infectious disease variant.

New SARS-CoV-2 variants arise frequently with different viral properties that can impact the effectiveness of the vaccines. Updating estimates of vaccine effectiveness (VE) in public health surveillance can be limited by the necessity of conducting a distinct study that entails analysis of prospective cohort data or using a test-negative design. We introduce a method for dynamically updating estimates of VE using data that accumulate in real time. Our method uses dynamic case-control sampling to estimate VE against a newly emerging variant relative to a previous variant. Dynamic case-control sampling is a technique that continuously updates VE estimates by comparing individuals infected with a newly emerging variant (defined as "cases") to those infected with a previously circulating variant (defined as "controls"). We use this estimate in combination with information about VE from the previous variant (these estimates are typically available from larger, traditional studies) to infer VE against the emerging variant. We demonstrate the utility of this method on the BA.1 and BA.2 sub-lineages of the Omicron variant. The method produces estimates of VE comparable to those produced using traditional methods, although with increased SE. The increase in error, however, is reasonable given a much smaller sample size than other studies, and error ranges of the estimates could be significantly improved by sequencing a larger proportion of identified cases. Our method, which assumes only a fraction of the new cases are being sequenced, can be applied by health departments using routinely collected data to produce timely, rigorous VE estimates to rapidly identify potential changes in VE.

Humans↗
PubMed2025

Reference-Free Variant Calling with Local Graph Construction with ska lo (SKA).

The study of genomic variants is increasingly important for public health surveillance of pathogens. Traditional variant-calling methods from whole-genome sequencing data rely on reference-based alignment, which can introduce biases and require significant computational resources. Alignment- and reference-free approaches offer an alternative by leveraging k-mer-based methods, but existing implementations often suffer from sensitivity limitations, particularly in high mutation density genomic regions. Here, we present ska lo, a graph-based algorithm that aims to identify within-strain variants in pathogen whole-genome sequencing data by traversing a colored De Bruijn graph and building variant groups (i.e. sets of variant combinations). Through in silico benchmarking and real-world dataset analyses, we demonstrate that ska lo achieves high sensitivity in single-nucleotide polymorphism (SNP) calls while also enabling the detection of insertions and deletions, as well as SNP positioning on a reference genome for recombination analyses. These findings highlight ska lo as a simple, fast, and effective tool for pathogen genomic epidemiology, extending the range of reference-free variant-calling approaches. ska lo is freely available as part of the SKA program (https://github.com/bacpop/ska.rust).

Polymorphism, Single Nucleotide↗
PubMed2025

How does date-rounding affect phylodynamic inference for public health?

Phylodynamic analyses infer epidemiological parameters from pathogen genome sequences for enhanced genomic surveillance in public health. Pathogen genome sequences and their associated sampling dates are the essential data in every analysis. However, sampling dates are usually associated with hospitalisation or testing and can sometimes be used to identify individual patients, posing a threat to patient confidentiality. To lower this risk, sampling dates are often given with reduced date-resolution to the month or year, which can potentially bias inference. Here, we introduce a practical guideline on when date-rounding biases the inference of epidemiologically important parameters across a diverse range of empirical and simulated datasets. We show that the direction of bias varies for different parameters, datasets, and tree priors, while compounding with lower date-resolution and higher substitution rates. We also find that bias decreases for datasets with longer sampling intervals, implying that our guideline is most applicable to emerging datasets. We conclude by discussing future solutions that prioritise patient confidentiality and propose a method for safer sharing of sampling dates that translates them them uniformly by a random number.

Humans↗
PubMed2026

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

Humans↗
PubMed2025

Applications and benefits of the British Society for Antimicrobial Chemotherapy Resistance Surveillance Project-legacy and future.

The BSAC Resistance Surveillance Project ran from 1999 to 2019, amassing an unrivalled collection of almost 100 000 bacterial isolates from bloodstream and lower respiratory tract infections in the UK and Ireland. It was initiated in response to increasing antimicrobial resistance and supplemented existing surveillance schemes, enhancing the understanding of resistance epidemiology by estimating species prevalence within collection groups together with levels of antibacterial resistance, presented in terms of MICs and percentage susceptibility for each species/antibiotic combination tested. Generated data were explored to monitor and identify factors shaping resistance trends, and to profile antibacterial resistance patterns in specific geographies, settings and patient populations. The release of data and/or bacterial isolates led to a rich repository of published peer-reviewed papers. Additionally, the promotion of the BSAC standardized susceptibility testing method resulted in greater uniformity of antimicrobial susceptibility testing in hospital microbiology laboratories. Over time, public health laboratories' surveillance systems became increasingly comprehensive, and the BSAC Project ceased in 2019. This invaluable collection is now housed in the University of Dundee, in collaboration with the University of St Andrews. We highlight the collection's unique timeliness, and how the BSAC Project contributed to key interventions for infection prevention and control, public health and antimicrobial stewardship. We demonstrate the utility and benefits of the Project outlining the collection's future applications as an important bioresource. It comprises well-defined bacterial isolates-many now sequenced-with MIC data and demographic information. This legacy is available to researchers via the Tayside Biorepository and custodian contacts.

Humans↗