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Review

Genomics in Epidemiology and Disease Surveillance: An Exploratory Analysis

by
Shraddha Tiwari
1,†,
Thakur Dhakal
1,†,
Baek-Jun Kim
2,†,
Gab Sue Jang
1,* and
Yeonsu Oh
3,*
1
Department of Life Sciences, Yeungnam University, Gyeongsan 38541, Republic of Korea
2
National Institute of Ecology, Seocheon 33657, Republic of Korea
3
College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University, Chuncheon 24341, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Life 2025, 15(12), 1848; https://doi.org/10.3390/life15121848 (registering DOI)
Submission received: 25 September 2025 / Revised: 26 November 2025 / Accepted: 26 November 2025 / Published: 1 December 2025
(This article belongs to the Section Genetics and Genomics)
Versions Notes

Abstract

Genomics has revolutionized epidemiology and disease surveillance by providing powerful tools for identifying, tracking, and analyzing pathogens at the molecular level. This exploratory analysis examines the integration of genomic with traditional epidemiological approaches, highlighting the role of whole-genome sequencing as a key method for pathogen identification, outbreak investigation, and understanding transmission dynamics. By enabling the detection of mutations and monitoring of antimicrobial resistance, genomics allows for precise mapping of infection sources and transmission pathways, thereby improving the timeliness and accuracy of public health responses. Furthermore, genomic surveillance supports the early detection of emerging variants, such as those observed during viral outbreaks like COVID-19, facilitating proactive intervention strategies. Despite its transformative potential, challenges related to data privacy, infrastructure, and interdisciplinary collaboration persist. This analysis emphasizes the importance of genomics in building resilient surveillance systems to address future infectious disease threats and advocates for sustained investment in genomic technologies to advance global health security.

1. Introduction

Emerging infectious diseases continue to pose significant challenges to global health and biosecurity, driven by factors such as population growth, globalization, urbanization, and increased human–animal interactions [1]. These factors facilitate cross-species transmission and the emergence of novel pathogens with pandemic potential. Genomic technologies have revolutionized infectious disease surveillance by enabling the rapid identification, characterization, and monitoring of pathogens with unprecedented precision [2]. In outbreak settings, genome sequencing aids in source tracing, predicting transmission routes, and guiding targeted public health interventions. Over the past decade, genomics has become a cornerstone of outbreak investigation, surpassing traditional diagnostic tools in detecting new pathogen strains, determining their genetic composition, and predicting antimicrobial resistance (AMR) [3,4]. By analyzing pathogen genomes, researchers can identify infection sources, track transmission pathways, and design more effective control strategies [5]. Unlike conventional epidemiological approaches that rely solely on case data, genomic epidemiology integrates sequencing data with contextual metadata (e.g., time and location) to reconstruct detailed transmission networks and outbreak dynamics [6]. This integration offers more precise, evidence-based insights into disease spread and enables timely, data-driven interventions.
Advances in sequencing technologies and bioinformatics, particularly whole-genome sequencing (WGS), have greatly enhanced the capacity for high-resolution pathogen tracking and comparative genomic analysis [7,8,9]. WGS offers single-nucleotide resolution and can be applied directly to clinical or environmental samples, including blood, swabs, feces, and tissue, enabling comprehensive detection even in cases of mixed infections. The typical workflow involves nucleic acid extraction, library preparation, and sequencing on platforms such as Illumina, PacBio, or Oxford Nanopore, followed by bioinformatics pipelines for genome assembly, variant calling, and phylogenetic inference [10,11]. Complementary approaches, such as metagenomic next-generation sequencing and quantitative polymerase chain reaction (qPCR)-informed sequencing, facilitate simultaneous detection and quantification of multiple pathogens within a single sample. These methods support infection burden estimation through read-depth and coverage-based analyses [10]. Comparative genomics further distinguishes outbreak-related strains from sporadic cases, maps transmission routes, and monitors AMR determinants—especially when integrated with temporal and geographic metadata for near real-time epidemiological reconstruction.
Recent developments in portable sequencing devices, such as the Oxford Nanopore MinION, have enabled on-site genomic surveillance during field outbreaks, including those involving Ebola, COVID-19, and African swine fever. These tools generate actionable genomic data within hours, enabling rapid public health responses and containment measures [12]. Overall, WGS and related genomic technologies form the backbone of modern disease surveillance by combining high-throughput sequencing with advanced analytics to deliver precise pathogen detection, quantitative infection assessment, and critical insights into pathogen evolution and transmission dynamics. The increasing accessibility and cost-effectiveness of these technologies highlight their indispensability for early outbreak detection and control, as well as for the sustained monitoring of endemic and emerging threats.
In an era marked by emerging and re-emerging infectious diseases, the integration of genomics into epidemiology represents a pivotal advancement in global health. This study examines how genomics can be applied to enhance the detection, understanding, and control of infectious diseases through the analysis of pathogen and host genetic material. It reviews the major applications of genomics in disease surveillance, evaluates its impact on public health systems, and identifies key challenges that must be addressed to strengthen global preparedness for future infectious threats.

2. Materials and Methods

We conducted a scoping review of peer-reviewed studies integrating genomic data with traditional epidemiological methods for infectious disease surveillance and outbreak investigation. A Google Scholar (https://scholar.google.com/) search on 1 August 2025 using the terms “genomic epidemiology,” “whole-genome sequencing,” “genomic surveillance,” “pathogen transmission,” “outbreak investigation,” and “antimicrobial resistance” identified 220 records. After screening and full-text assessment, 30 articles met inclusion criteria, applying genomic methods such as WGS, phylogenetics, or phylodynamic and integrating findings with tools including contact tracing, case investigations, serology, or spatiotemporal analyses. Owing to heterogeneity across pathogens, settings, and analytical pipelines, we synthesized results narratively by use cases such as outbreak detection, transmission reconstruction, variant signal detection, and AMR monitoring and summarized key implementation considerations in epidemiology and pathogen surveillance.

3. Genomic Surveillance and Transmission Dynamics of Infectious Diseases

WGS of pathogenic genomes, combined with bioinformatics analyses, has significantly improved the speed and precision of genomic applications in outbreak tracking and infection control. Infectious diseases remain a leading cause of mortality worldwide, with viruses, bacteria, and protozoa exhibiting high mutation rates [13,14]. These rapid evolutionary changes contribute to the emergence of new human infections, more virulent strains, and antibiotic- or drug-resistant organisms. In this context, genomic surveillance serves three primary purposes: (i) to enable global monitoring of pathogens through WGS, (ii) to elucidate the development and spread of drug resistance and infectious agents, and (iii) to provide data that guide public health interventions [12,15].
Globalization and international travel complicate outbreak detection by accelerating cross-border transmission, while the co-circulation of multiple genotypes in endemic regions fosters gene flow and the emergence of variants with novel phenotypes [16]. WGS and population genomics address these challenges by resolving fine-scale spatiotemporal patterns, quantifying genetic relatedness and geographic structure, and clarifying evolutionary dynamics [17,18]. As genomic datasets expand and become increasingly integrated into surveillance systems, they continue to strengthen outbreak detection, source attribution, and response strategies [18,19].
Malaria exemplifies the value of WGS-guided surveillance and transmission analysis. Along the China–Myanmar border, Plasmodium vivax isolates formed a distinct local lineage, revealing ancestral uniqueness and informing geographically tailored elimination strategies [19,20,21]. Genomic analyses have also clarified parasite movement and evolution. In Malaysian Borneo, introgression between two Plasmodium knowlesi subpopulations associated with different macaque hosts influenced mosquito transmission potential [22]. Global P. vivax data indicate a South Asian origin with declining genetic diversity away from Southeast Asia, consistent with a founder effect [23]. Similarly, P. simium likely evolved from New World P. vivax through human-to-primate transmission. Moreover, machine learning applied to WGS data has identified P. vivax infections imported into Africa from South Asia, improving the targeting of control resources [24]. Despite sustained elimination efforts, persistent reservoirs and cross-border introductions continue to sustain transmission risk, underscoring the importance of continuous WGS monitoring for malaria control and eradication [19,20].
Arboviral genomics has likewise advanced outbreak investigations. Dengue virus (DENV-1 to DENV-4), which is endemic in more than 129 countries with major burdens in Asia, South America, and the Western Pacific [25,26], has shown increasing incidence in Africa and heightened risk in Europe and North America due to climate change and the expansion of Aedes albopictus [26,27]. In Florida, record dengue activity from 2022 to 2023 included both travel-related and locally acquired cases; sequencing revealed DENV-1 and DENV-3 genomes clustering with strains from India (2019), while DENV-2 aligned with a 2018 China strain, highlighting the role of international travel in sustaining transmission [28]. For the West Nile virus (WNV), Europe has experienced at least 13 independent introductions of lineages 1a and 2, with lineage 2 dominant in temperate regions such as Germany and the Netherlands; these patterns, integrated into platforms such as Nextstrain, illuminate ongoing geographic expansion and viral epidemiology [29,30,31]. A cross-pathogen summary of representative WGS applications, including regional contexts, key genomic findings, and associated public health impacts, is presented in Table 1.
Bacterial genomics has significantly improved the detection of introductions and high-risk lineages. Highly virulent methicillin-resistant Staphylococcus aureus (MRSA) strains, such as CC239/ST239, are widespread in Asia. In Denmark, most MRSA ST239 cases were associated with travelers returning from Asia, Africa, or the Middle East, demonstrating how WGS supports travel-related source attribution and facilitates timely infection control [33]. During hemolytic uremic syndrome epidemics, WGS of Shiga toxin-producing Escherichia coli has enabled real-time monitoring of emerging strains, differentiation between localized and widespread outbreaks, and mapping of transmission routes—critical steps in reducing pediatric mortality [45,46]. Genomic analysis of E. coli O104:H4 associated with the 2009–2011 fenugreek seed outbreak traced its origin to Egypt and revealed close genetic relatedness between French and German cases, with minor genomic variations explaining divergent epidemic trajectories [16].
Protozoan and fungal pathogens have also benefited from genomic surveillance. WGS of Cryptosporidium hominis subtype If A12G1R5 identified three genetically distinct, low-diversity subpopulations, suggesting the recent emergence of a hypertransmissible lineage in the United States and enabling targeted public health interventions [47]. Similarly, genomic studies of Candida auris in India revealed clonal isolates with low diversity across patients and hospitals. The presence of the MATα mating locus and transporter genes (ABC and MFS) may contribute to multidrug resistance, while its genetic distinctness from other Candida species indicates an independent evolutionary trajectory potentially linked to antifungal overuse [34].
Viral transmission dynamics can be elucidated through lineage-aware, region-specific analyses. For example, since its first identification in 1953, the Chikungunya virus has exhibited distinct regional patterns enzootic spillover in West Africa, recurrent introductions into East Africa from Asia (including the Indian Ocean lineage), and limited interregional viral flow all clarified by genomic studies [14,48,49]. These insights reveal hidden transmission routes and the evolutionary pressures shaping epidemic potential. Tuberculosis surveillance increasingly employs WGS to dissect transmission pathways and identify genetic determinants of fitness. Longitudinal tracking of the Mycobacterium tuberculosis Zaragoza (MtZ) strain, implicated in 242 cases between 2004 and 2020, identified single-nucleotide polymorphisms in genes related to virulence, pathogenesis, and survival, as well as additional mutations that may account for its unusually high transmissibility. Such findings enable more precise, strain-specific public health interventions.
Collectively, these applications illustrate how WGS and population genomics can resolve fine-scale spatiotemporal transmission patterns, uncover origins and introductions (including travel-associated events), identify resistance and virulence determinants, and distinguish localized clusters from widespread dissemination. As WGS capacity expands and global datasets grow, the integration of genomic, epidemiologic, and mobility data through phylogenomics, phylodynamics, and machine learning will remain essential for early detection of emerging threats, optimization of control strategies, and sustained progress toward disease elimination [14,49,50].

4. Genomics in Determinants of Drug Resistance

The widespread use of antimicrobial agents exerts strong selective pressure on pathogen populations by eliminating susceptible strains, thereby creating a population bottleneck. This process favors the survival of resistant variants and increases the frequency of drug-resistance alleles at specific loci and across linked haplotypes [14]. The emergence and spread of AMR have become one of the most pressing challenges in infectious disease control and global public health protection [51]. Advances in diagnostic microbiology have increasingly integrated conventional techniques with WGS and bioinformatics analyses, enabling the rapid identification of novel AMR genes and providing insights into their mechanisms of dissemination [52].
Recent WGS investigations have revealed numerous AMR determinants across bacterial pathogens. For example, Neisseria gonorrhoeae has acquired novel mtrR mutations and penA alleles conferring resistance to azithromycin and cefixime [53]. Mutations in the 23S rRNA gene of Helicobacter pylori have been linked to clarithromycin resistance, whereas Proteus species have exhibited florfenicol resistance mediated by the floR gene located on both plasmids and chromosomes underscoring the role of mobile genetic elements in resistance gene transfer [54,55]. Similarly, genome sequencing of multidrug-resistant Staphylococcus lentus identified 11 resistance genes distributed across plasmids and chromosomes [56], while tetracycline-resistant Arthrobacter nicotianae carried eight resistance genes, including mobile loci located outside plasmids [57].
Beyond gene discovery, WGS plays a central role in characterizing the genomic epidemiology of AMR. Analysis of 150 N. gonorrhoeae isolates from Ukraine revealed resistance to ciprofloxacin, tetracycline, and benzylpenicillin, with phylogenomic clustering into six major groups associated with multidrug-resistant lineages [58]. Mutations in gyrA S91F and parC S87R were correlated with ciprofloxacin resistance, while rpsJ V57M, tetM, and mosaic penA-34.001 contributed to tetracycline and β-lactam resistance. These findings demonstrate how genomics can link specific genetic markers to resistance phenotypes, thereby improving AMR monitoring, surveillance, and outbreak management.
Genomic approaches have also deepened the understanding of drug resistance in parasitic diseases. In Plasmodium falciparum, the pfcrt K76T mutation has long served as a key marker of chloroquine resistance. Genome-wide association studies (GWAS) have identified additional variants, including pfcrt C350R and pfaat1 S258L, which are associated with chloroquine resistance in French Guiana and The Gambia, respectively [59]. Beyond traditional GWAS, population genetic tools such as linkage disequilibrium mapping, site frequency spectrum analysis, and machine learning are increasingly employed to identify genetic drivers of resistance and predict emerging resistance mechanisms [60]. Open datasets for P. falciparum and P. vivax have further enabled global surveillance by cataloging resistance markers such as pfdhfr, pfdhps, pfk13, pfmdr1, pfcrt, and pfama1, thereby informing malaria control strategies [61].
In addition to bacterial and parasitic pathogens, WGS has provided valuable insights into resistance mechanisms in opportunistic organisms such as Enterococcus faecium. A large-scale study of 1025 bloodstream isolates from Australia identified three major genomic clusters, including both clonal complex (CC) and non-CC strains, many of which carried vanA and vanB operons conferring vancomycin resistance [62]. Distinct subclusters were associated with specific geographic regions, highlighting the value of genomic epidemiology in contextualizing resistance dynamics and predicting future trends.
Collectively, these findings highlight the transformative role of WGS in mapping the genetic determinants of drug resistance. By linking genotype to phenotype through comparative genomics, GWAS, and machine learning, genomics not only enables the discovery of novel biomarkers but also strengthens resistance surveillance systems. The integration of genomic data into public health frameworks remains essential for combating the growing threat of AMR worldwide. Representative examples of pathogens, resistance determinants, and the contributions of WGS to understanding drug resistance are summarized in Table 2.

5. Role of Genomics in Epidemiological Surveillance

Epidemiological surveillance involves the systematic collection, analysis, and dissemination of health data to support the planning, implementation, and evaluation of public health programs. It requires continuous monitoring of health events within populations and is typically integrated into health care systems to track major public health issues. The primary goal of surveillance is to strengthen disease monitoring and related public health activities [71]. When genomic and epidemiological data are combined, they provide near-real-time insights into outbreaks, enabling timely and targeted interventions to protect communities. Implementing genomic epidemiology across different regions and levels of health care requires coordinated collaboration to fully harness its potential for mitigating outbreaks, tracking pathogen spread, and containing emerging infectious variants.
The integration of genomic data into surveillance systems has significant practical implications, particularly for infectious disease control. WGS enables direct analysis of pathogens from clinical samples, offering critical information during outbreaks—especially regarding mutations that influence virulence, drug resistance, and antigenicity [72]. These data also enhance molecular diagnostics at the point of care and support the development of personalized treatment strategies, paralleling precision medicine approaches. At the population level, combining genomic and epidemiological data facilitates the identification of pathogen mutations associated with transmission events, providing detailed insights into infection and transmission dynamics. This approach enables the design of more precise and targeted public health interventions, surpassing the capabilities of traditional surveillance systems. Moreover, genomic epidemiology aligns with the One Health framework, which recognizes the interconnectedness of human, animal, and environmental health, thereby advancing disease surveillance, prevention, and control within a broader ecological context [4].
The World Health Organization (WHO) emphasizes the critical role of epidemiological surveillance in defining health problems, understanding established and emerging diseases, and guiding effective interventions [73]. Comprehensive surveillance systems capture essential data on pathogen clones and lineages, clinical manifestations, affected populations, and morbidity and mortality rates. Such information is fundamental for evidence-based decision-making, efficient resource allocation, and the design of effective public health interventions. Continuous surveillance is also vital for monitoring and evaluating ongoing programs [74]. Accurate systems that track the geographic spread of diseases enable assessment of health event significance and provide a foundation for informed policy and funding decisions. Genomic surveillance has proven highly effective in detecting outbreaks, tracing their origins, and monitoring AMR trends [75,76]. Long-term AMR studies highlight the value of integrating routine genomic surveillance into national and global health strategies, particularly for guiding treatment protocols and vaccination programs. Genomic data facilitate the identification of emerging AMR lineages, tracking of mobilizable resistance genes, and evaluation of genotypic resistance predictions, although limitations remain for certain pathogens [77,78]. The United Kingdom’s experience demonstrates that routine genomic sequencing of Salmonella has identified more outbreaks than traditional microbiological methods, illustrating the potential of genomics to enhance outbreak detection and intervention prioritization [79].
Global initiatives have increasingly standardized AMR surveillance for health care-associated infections, providing insights into dominant regional lineages and situating them within a global context [80]. Identifying emerging and prevalent AMR lineages worldwide is crucial for guiding research, informing public health responses, and optimizing control strategies. By integrating genomics into epidemiological surveillance, public health systems can detect outbreaks more rapidly, better understand transmission dynamics, and implement interventions more effectively—ultimately strengthening global health security.

6. Advances in Genomic Epidemiology and Pathogen Surveillance

The COVID-19 pandemic marked a pivotal turning point in global genomic surveillance. Before 2020, genomic surveillance data were rarely available to the public; however, the rapid release of SARS-CoV-2 genomic sequences beginning on 10 January 2020, fundamentally changed this landscape. Within days, diagnostic tests were developed, with the first available by 13 January 2020 [81]. Open access to genomic epidemiological data proved critical for reconstructing transmission patterns, guiding intervention strategies, and informing control measures [82,83]. Genomic surveillance played a central role in the pandemic response by monitoring viral evolution, tracking transmission pathways, and identifying emerging variants that shaped diagnostic, therapeutic, and vaccine development.
Throughout the pandemic, the International Health Regulations (2005), Emergency Committee of the WHO emphasized the importance of strengthening genomic surveillance systems. Similarly, the WHO-convened Independent Panel for Pandemic Preparedness and Response called for sustained global investment to expand sequencing capacity. Although achieving equitable access to genomic technologies remains a challenge, maintaining progress in genomic surveillance is essential to strengthening global health security [84]. In response, the WHO launched the Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential in March 2022, developed through extensive consultation with global, regional, and national stakeholders including public health institutions, One Health partners, and WHO technical programs the strategy outlines a 10-year roadmap for integrating genomic data into global health systems [84, 85 ].
The regional implementation of this strategy has been adapted to local needs and capacities. For instance, investments in sub regional hubs have enhanced genomic surveillance infrastructure in Europe and Africa, while in densely populated regions such as Southeast Asia, efforts focus on leveraging regional assets through consortia of policymakers, researchers, and private partners. In the Eastern Mediterranean, where political instability and humanitarian crises often impede progress, external support has been essential for establishing reference laboratories and fostering international collaboration [85]. These regionally tailored approaches allow countries to expand genomic sequencing capacity incrementally while aligning implementation with national and regional health priorities.
Technological innovations in communication have also reshaped pathogen surveillance. The widespread use of the internet, smartphones, and social media has improved both the speed and accuracy of disease detection. By integrating traditional surveillance with digital health tools such as online self-reporting systems, internet search data, and social media analytics public health authorities can identify early signals of outbreaks more rapidly [86,87,88]. These digital approaches complement genomic sequencing by providing real-time epidemiological context and broadening the reach of surveillance systems.
In parallel, WGS has advanced the prediction and characterization of AMR. Tools such as AMRFinderPlus, ResFinder, and the Comprehensive Antibiotic Resistance Database (CARD) integrate genomic data with bioinformatics to identify AMR genes, resistance-associated mutations, and gene classes [52,77]. CARD, in particular, combines curated genetic data with machine learning through CARD*Shark, which extracts information from the scientific literature. Its Resistance Gene Identifier, aligned with the Antibiotic Resistance Ontology, provides detailed insights into AMR determinants [77]. The incorporation of machine learning has enhanced the predictive accuracy and versatility of these tools across diverse pathogens and antimicrobial classes.
The rise of big data and artificial intelligence (AI) has further amplified the capabilities of genomic epidemiology. Advances in sequencing and digital health technologies have generated vast datasets that integrate genomic information, clinical records, imaging, and laboratory results. AI and machine learning algorithms are increasingly employed to uncover hidden correlations within these datasets, enabling more precise identification of biomarkers and disease associations [89]. In epidemiological surveillance, the integration of big data analytics and AI not only accelerates pathogen detection and resistance profiling but also strengthens predictive and diagnostic capacities for future outbreaks [90].

7. Challenges in Genomic Epidemiology

Genomic epidemiology provides substantially higher resolution than traditional surveillance methods, such as case-based reporting, exposure histories, contact tracing, serotyping, pulsed-field gel electrophoresis, multilocus sequence typing, and culture-based antimicrobial susceptibility testing. By resolving fine-scale transmission links, distinguishing co-circulating clusters, identifying introductions, and directly detecting AMR and virulence determinants, WGS enhances outbreak detection and accelerates targeted interventions compared with the discriminatory power and timelines of conventional methods [4]. Standardized data formats and analytical frameworks further promote cross-border comparability, enabling robust global situational awareness despite heterogeneity in diagnostics and case definitions. Moreover, phylogenetic and phylodynamic analyses provide critical evolutionary context, quantifying lineage turnover, recombination, and fitness changes that traditional surveillance methods cannot easily infer. These insights support vaccine and diagnostic development, as well as risk assessment [91,92,93]. However, the advantages of genomic epidemiology come with distinctive biases and operational challenges that must be carefully managed.
Compared with conventional surveillance systems, genomic inference is particularly sensitive to sampling and ascertainment biases. Sequencing efforts often over-represent severe, hospitalized, or urban cases, skewing lineage frequencies, molecular clock estimates, and transmission reconstructions. Although traditional case-based systems are also affected by care-seeking behavior and testing access, they generally maintain broader, albeit lower-resolution, coverage [94,95]. Temporal and geographic disparities in sequencing intensity can generate misleading signals of introductions or lineage extinction [96,97]. Low-pathogen-load samples (high cycle threshold [Ct] values) are frequently under sequenced, enriching datasets for acute or severe infections and biasing evolutionary and transmission inferences. Additionally, culture- or amplicon-based protocols may distort genetic diversity through in vitro adaptation or primer dropout [98,99]. Further biases arise from reference genome selection and assembly approaches. Mapping to distant reference genomes can result in the omission of novel segments or structural variants, while de novo assembly may misplace mobile genetic elements.
Recombination and within-host diversity also violate the tree-like assumptions of phylogenetic inference and, when combined with transmission bottlenecks, can obscure directionality and donor–recipient relationships that cannot be resolved from genomic data alone [100,101,102]. Additionally, model and annotation uncertainties—spanning lineage assignment, molecular clock calibration, and genotype–phenotype prediction can limit interpretability. AMR genotypes may not fully reflect phenotypic resistance due to gene expression variability or epistatic interactions. Laboratory contamination and index hopping, particularly in low-biomass samples, can generate false genomic links that are not observed in aggregated traditional surveillance data [103]. Furthermore, because genomes serve as high-resolution identifiers when paired with detailed metadata, privacy and reidentification risks surpass those typical of aggregate case reporting, necessitating stronger data governance and ethical oversight [4,104].
Mitigating these biases requires deliberate attention to study design, methodology, and interpretation. Prospective sampling frameworks that define geography, time frame, disease severity, and setting—combined with transparent reporting of denominators and minimal interoperable metadata (e.g., onset date, Ct value, travel history, care setting, and outcomes)—can stabilize phylodynamic inferences and contextualize detected clusters [103,105,106]. Wet-lab improvements, such as optimized library preparation for high-Ct samples, hybrid capture to recover low-titer genomes, updated primer schemes, replicate sequencing, and orthogonal validation assays, help reduce culture- and amplicon-derived artifacts. Analytically, the use of lineage-appropriate or graph-based references, recombination-aware phylogenetic models, haplotype-based inference, long-read confirmation where feasible, and standardized quality control pipelines with contamination safeguards can minimize reference, assembly, and crosstalk biases [105,107]. Operationally, defining explicit action thresholds such as single-nucleotide polymorphism distance and cluster growth rate criteria calibrated to substitution rates and transmission contexts—should be undertaken collaboratively with field epidemiologists. Such alignment ensures that genomic signals translate into timely and proportionate public health interventions rather than remaining confined to academic interpretation [108].
Real-world implementation also depends on infrastructure, workforce capacity, sustainability, and governance—domains in which traditional surveillance systems often face fewer barriers. Despite declining sequencing costs, many low- and middle-income countries remain underequipped, lacking sufficient laboratory facilities, sequencing platforms, bioinformatics capacity, secure data infrastructure, and trained personnel, with sustainable funding remaining limited. For example, Algeria has only one sequencing center and minimal PCR capacity, serving approximately 45 million inhabitants. During the early COVID-19 pandemic, in-country limitations at the Pasteur Institute necessitated shipping samples to the Pasteur Institute in France, delaying national data generation and public health responses. These gaps highlight the need for regional sequencing hubs, pooled procurement systems, diversified sequencing platforms, strategic buffer stocks, cloud-based analytical resources, open-source and standardized bioinformatics pipelines, and targeted training programs spanning molecular biology, bioinformatics, and epidemiology. Equally important are standard operating procedures and decision-support frameworks that integrate genomics into routine surveillance systems. Embedding genomic data into existing workflows ensures that phylogenetic findings translate into actionable public health measures, similar to how traditional incidence thresholds are institutionalized in surveillance programs [109].
Governance, data sharing, and incentive structures are also pivotal to effective implementation. Political, legal, and ethical challenges related to data ownership, attribution, privacy, intellectual property, and national security can hinder cross-border genomic data exchange essential for phylodynamic and risk assessments, even when traditional reporting occurs under the International Health Regulations. Transparent data-use agreements, privacy-preserving architectures with tiered access, attribution norms, and benefit-sharing frameworks can improve timeliness, enhance trust, and protect both individual and national interests [104,109]. Since genomic data combined with rich metadata increase the risk of reidentification, privacy-by-design principles and robust ethical oversight must accompany the scale-up of genomic surveillance systems [104].
The COVID-19 pandemic further demonstrated that human-only surveillance can overlook critical animal and environmental reservoirs, including reverse zoonotic transmission pathways that may reshape viral evolution and generate novel variants [110]. Adopting a One Health framework that integrates human, veterinary, and environmental genomics can uncover hidden transmission routes, anticipate spillover events, and strengthen pandemic preparedness by broadening sampling coverage and analytical integration across species and ecosystems [111]. Emerging computational approaches also enhance these capabilities: AI and machine learning can prioritize samples for sequencing, detect anomalous transmission patterns in real time, and predict viral evolution related to virulence, resistance, or antigenic drift. However, to be operationally effective, such models must remain transparent, well-calibrated, and continuously validated in public health contexts, with clear communication of uncertainty and alignment with decision-making frameworks [112]. Expanding and standardizing epidemiological metadata including infection source, travel history, hospitalization metrics, and outcomes will further contextualize genomic findings and refine public health priorities.
Ultimately, genomics complements rather than replaces traditional surveillance methods. When integrated with case surveillance, contact tracing, mobility data, environmental monitoring, and field investigations, WGS provides a high-resolution perspective that can validate, refine, or redirect public health interventions. Realizing its full potential requires addressing infrastructure and workforce gaps, embedding ethical safeguards and interoperable data standards, and developing joint analytic and operational playbooks to ensure that genomic insights translate into timely, equitable, and effective actions. With sustained investment, interdisciplinary coordination, and equitable access, genomic epidemiology can fulfill its promise of transforming disease surveillance and response across both routine and emergency contexts [104,109,112].

8. Conclusions

The integration of genomics into public health surveillance has fundamentally transformed the management of infectious diseases, providing unprecedented precision in detecting, tracking, and responding to pathogens. This review highlights the pivotal role of WGS and other genomic technologies in advancing infectious disease surveillance and epidemiology. Evidence consistently demonstrates that genomics delivers high-resolution insights into pathogen transmission, evolution, and AMR, thereby enhancing the accuracy and effectiveness of public health interventions. However, realizing the full potential of these technologies requires addressing persistent challenges related to infrastructure, data governance, workforce capacity, and equitable access. Continued investment, interdisciplinary collaboration, and global coordination are essential to harness genomics for building resilient, data-driven public health systems capable of responding effectively to both current and emerging infectious disease threats. By enabling more timely, accurate, and targeted interventions, genomics has become an indispensable component of modern outbreak response strategies. Sustained efforts to strengthen infrastructure, promote equitable access, and foster cross-disciplinary collaboration will be critical for maximizing its long-term impact. This study serves as a resource for scholars, researchers, and policymakers seeking to understand how genomics can shape effective, data-driven infectious disease surveillance systems and inform strategies for global health preparedness.

Author Contributions

S.T., G.S.J. and Y.O.: Conceptualization; S.T., B.-J.K. and T.D.: Methodology, Writing—Original Draft Preparation, Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Institute of Ecology with funding from the Ministry of Environment of the Republic of Korea (NIE-B-2025-44) and by the Government-wide R&D to Advance Infectious Disease Prevention and Control, Republic of Korea (RS-2023-KH140418).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

During the preparation of this work the authors used ChatGPT4o from OpenAI in order to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Jones, K.E.; Patel, N.G.; Levy, M.A.; Storeygard, A.; Balk, D.; Gittleman, J.L.; Daszak, P. Global Trends in Emerging Infectious Diseases. Nature 2008, 451. [Google Scholar] [CrossRef] [PubMed]
  2. Nwadiugwu, M.C.; Monteiro, N. Applied Genomics for Identification of Virulent Biothreats and for Disease Outbreak Surveillance. Postgrad. Med. J. 2023, 99, 403–410. [Google Scholar] [CrossRef] [PubMed]
  3. Tang, P.; Gardy, J.L. Stopping Outbreaks with Real-Time Genomic Epidemiology. Genome Med. 2014, 6, 104. [Google Scholar] [CrossRef]
  4. Gardy, J.L.; Loman, N.J. Towards a Genomics-Informed, Real-Time, Global Pathogen Surveillance System. Nat. Rev. Genet. 2018, 19, 9–20. [Google Scholar] [CrossRef]
  5. Popovich, K.J.; Snitkin, E.S. Whole Genome Sequencing—Implications for Infection Prevention and Outbreak Investigations. Curr. Infect. Dis. Rep. 2017, 19, 15. [Google Scholar] [CrossRef]
  6. Biek, R.; Pybus, O.G.; Lloyd-Smith, J.O.; Didelot, X. Measurably Evolving Pathogens in the Genomic Era. Trends Ecol. Evol. 2015, 30, 306–313. [Google Scholar] [CrossRef] [PubMed]
  7. Armstrong, G.L.; MacCannell, D.R.; Taylor, J.; Carleton, H.A.; Neuhaus, E.B.; Bradbury, R.S.; Posey, J.E.; Gwinn, M. Pathogen Genomics in Public Health. N. Engl. J. Med. 2019, 381, 2569. [Google Scholar] [CrossRef]
  8. Black, A.; MacCannell, D.R.; Sibley, T.R.; Bedford, T. Ten Recommendations for Supporting Open Pathogen Genomic Analysis in Public Health. Nat. Med. 2020, 26, 832–841. [Google Scholar] [CrossRef]
  9. Vashisht, V.; Vashisht, A.; Mondal, A.K.; Farmaha, J.; Alptekin, A.; Singh, H.; Ahluwalia, P.; Srinivas, A.; Kolhe, R. Genomics for Emerging Pathogen Identification and Monitoring: Prospects and Obstacles. BioMedInformatics 2023, 3, 1145–1177. [Google Scholar] [CrossRef]
  10. Hilt, E.E.; Ferrieri, P. Next Generation and Other Sequencing Technologies in Diagnostic Microbiology and Infectious Diseases. Genes 2022, 13, 1566. [Google Scholar] [CrossRef]
  11. MacCannell, D. Next Generation Sequencing in Clinical and Public Health Microbiology. Clin. Microbiol. Newsl. 2016, 38, 169–176. [Google Scholar] [CrossRef]
  12. Daszak, P.; Cunningham, A.A.; Hyatt, A.D. Emerging Infectious Diseases of Wildlife—Threats to Biodiversity and Human Health. Science 2000, 287, 443–449. [Google Scholar] [CrossRef]
  13. Morens, D.M.; Folkers, G.K.; Fauci, A.S. The Challenge of Emerging and Re-Emerging Infectious Diseases. Nature 2004, 430, 242–249. [Google Scholar] [CrossRef]
  14. Wilson, B.A.; Garud, N.R.; Feder, A.F.; Assaf, Z.J.; Pennings, P.S. The Population Genetics of Drug Resistance Evolution in Natural Populations of Viral, Bacterial and Eukaryotic Pathogens. Mol. Ecol. 2015, 25, 42. [Google Scholar] [CrossRef]
  15. Morse, S.S. Factors in the Emergence of Infectious Diseases. Emerg. Infect. Dis. 1995, 1, 7. [Google Scholar] [CrossRef]
  16. Huang, W.; Guo, Y.; Lysen, C.; Wang, Y.; Tang, K.; Seabolt, M.H.; Yang, F.; Cebelinski, E.; Gonzalez-Moreno, O.; Hou, T.; et al. Multiple Introductions and Recombination Events Underlie the Emergence of a Hyper-Transmissible Cryptosporidium Hominis Subtype in the USA. Cell Host Microbe 2023, 31, 112–123.e4. [Google Scholar] [CrossRef] [PubMed]
  17. Hamilton, W.L.; Amato, R.; van der Pluijm, R.W.; Jacob, C.G.; Quang, H.H.; Thuy-Nhien, N.T.; Hien, T.T.; Hongvanthong, B.; Chindavongsa, K.; Mayxay, M.; et al. Evolution and Expansion of Multidrug-Resistant Malaria in Southeast Asia: A Genomic Epidemiology Study. Lancet Infect. Dis. 2019, 19, 943–951. [Google Scholar] [CrossRef]
  18. Tessema, S.K.; Raman, J.; Duffy, C.W.; Ishengoma, D.S.; Amambua-Ngwa, A.; Greenhouse, B. Applying Next-Generation Sequencing to Track Falciparum Malaria in Sub-Saharan Africa. Malar. J. 2019, 18, 268. [Google Scholar] [CrossRef] [PubMed]
  19. Neafsey, D.E.; Taylor, A.R.; MacInnis, B.L. Advances and Opportunities in Malaria Population Genomics. Nat. Rev. Genet. 2021, 22, 502–517. [Google Scholar] [CrossRef] [PubMed]
  20. Adam, I.; Alam, M.S.; Alemu, S.; Amaratunga, C.; Amato, R.; Andrianaranjaka, V.; Anstey, N.M.; Aseffa, A.; Ashley, E.; Assefa, A.; et al. An Open Dataset of Plasmodium Vivax Genome Variation in 1,895 Worldwide Samples. Wellcome Open Res. 2022, 7, 136. [Google Scholar] [CrossRef]
  21. Brashear, A.M.; Fan, Q.; Hu, Y.; Li, Y.; Zhao, Y.; Wang, Z.; Cao, Y.; Miao, J.; Barry, A.; Cui, L. Population Genomics Identifies a Distinct Plasmodium vivax Population on the China-Myanmar Border of Southeast Asia. PLoS Negl. Trop. Dis. 2020, 14, e0008506. [Google Scholar] [CrossRef]
  22. Divis, P.C.S.; Duffy, C.W.; Kadir, K.A.; Singh, B.; Conway, D.J. Genome-Wide Mosaicism in Divergence between Zoonotic Malaria Parasite Subpopulations with Separate Sympatric Transmission Cycles. Mol. Ecol. 2018, 27, 860–870. [Google Scholar] [CrossRef]
  23. Daron, J.; Boissière, A.; Boundenga, L.; Ngoubangoye, B.; Houze, S.; Arnathau, C.; Sidobre, C.; Trape, J.F.; Durand, P.; Renaud, F.; et al. Population Genomic Evidence of Plasmodium vivax Southeast Asian Origin. Sci. Adv. 2021, 7, eabc3713. [Google Scholar] [CrossRef]
  24. Trimarsanto, H.; Amato, R.; Pearson, R.D.; Sutanto, E.; Noviyanti, R.; Trianty, L.; Marfurt, J.; Pava, Z.; Echeverry, D.F.; Lopera-Mesa, T.M.; et al. A Molecular Barcode and Web-Based Data Analysis Tool to Identify Imported Plasmodium vivax Malaria. Commun. Biol. 2022, 5, 1411. [Google Scholar] [CrossRef]
  25. Guzman, M.G.; Harris, E. Dengue. Lancet 2015, 385, 453–465. [Google Scholar] [CrossRef] [PubMed]
  26. Zerfu, B.; Kassa, T.; Legesse, M. Epidemiology, Biology, Pathogenesis, Clinical Manifestations, and Diagnosis of Dengue Virus Infection, and Its Trend in Ethiopia: A Comprehensive Literature Review. Trop. Med. Health 2023, 51, 11. [Google Scholar] [CrossRef] [PubMed]
  27. Gainor, E.M.; Harris, E.; Labeaud, A.D. Uncovering the Burden of Dengue in Africa: Considerations on Magnitude, Misdiagnosis, and Ancestry. Viruses 2022, 14, 233. [Google Scholar] [CrossRef]
  28. Napit, R.; Elong Ngono, A.; Mihindukulasuriya, K.A.; Pradhan, A.; Khadka, B.; Shrestha, S.; Droit, L.; Paredes, A.; Karki, L.; Khatiwada, R.; et al. Dengue Virus Surveillance in Nepal Yields the First On-Site Whole Genome Sequences of Isolates from the 2022 Outbreak. BMC Genom. 2024, 25, 998. [Google Scholar] [CrossRef]
  29. Hadfield, J.; Brito, A.F.; Swetnam, D.M.; Vogels, C.B.F.; Tokarz, R.E.; Andersen, K.G.; Smith, R.C.; Bedford, T.; Grubaugh, N.D. Twenty Years of West Nile Virus Spread and Evolution in the Americas Visualized by Nextstrain. PLoS Pathog. 2019, 15, e1008042. [Google Scholar] [CrossRef]
  30. Schneider, J.; Bachmann, F.; Choi, M.; Kurvits, L.; Schmidt, M.L.; Bergfeld, L.; Meier, I.; Zuchowski, M.; Werber, D.; Hofmann, J.; et al. Autochthonous West Nile Virus Infection in Germany: Increasing Numbers and a Rare Encephalitis Case in a Kidney Transplant Recipient. Transbound. Emerg. Dis. 2021, 69, 221. [Google Scholar] [CrossRef] [PubMed]
  31. Vlaskamp, D.R.M.; Thijsen, S.F.T.; Reimerink, J.; Hilkens, P.; Bouvy, W.H.; Bantjes, S.E.; Vlaminckx, B.J.M.; Zaaijer, H.; van den Kerkhof, H.H.T.C.; Raven, S.F.H.; et al. First Autochthonous Human West Nile Virus Infections in the Netherlands, July to August 2020. Eurosurveillance 2020, 25, 2001904. [Google Scholar] [CrossRef]
  32. Koch, R.T.; Erazo, D.; Folly, A.J.; Johnson, N.; Dellicour, S.; Grubaugh, N.D.; Vogels, C.B.F. Genomic Epidemiology of West Nile Virus in Europe. One Health 2024, 18, 100664. [Google Scholar] [CrossRef]
  33. Coppens, J.; Xavier, B.B.; Vlaeminck, J.; Larsen, J.; Lammens, C.; Van Puyvelde, S.; Goossens, H.; Larsen, A.R.; Malhotra-Kumar, S. Genomic Analysis of Methicillin-Resistant Staphylococcus Aureus Clonal Complex 239 Isolated from Danish Patients with and without an International Travel History. Front. Microbiol. 2022, 13, 1016829. [Google Scholar] [CrossRef]
  34. Sharma, C.; Kumar, N.; Pandey, R.; Meis, J.F.; Chowdhary, A. Whole Genome Sequencing of Emerging Multidrug Resistant Candida Auris Isolates in India Demonstrates Low Genetic Variation. New Microbes New Infect. 2016, 13, 77–82. [Google Scholar] [CrossRef] [PubMed]
  35. Nicolas-Chanoine, M.H.; Bertrand, X.; Madec, J.Y. Escherichia Coli ST131, an Intriguing Clonal Group. Clin. Microbiol. Rev. 2014, 27, 543. [Google Scholar] [CrossRef] [PubMed]
  36. Campbell, A.M.; Hauton, C.; van Aerle, R.; Martinez-Urtaza, J. Eco-Evolutionary Drivers of Vibrio Parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach. JMIR Bioinform. Biotech. 2024, 5, e62747. [Google Scholar] [CrossRef] [PubMed]
  37. Trees, E.; Carleton, H.A.; Folster, J.P.; Gieraltowski, L.; Hise, K.; Leeper, M.; Nguyen, T.A.; Poates, A.; Sabol, A.; Tagg, K.A.; et al. Genetic Diversity in Salmonella Enterica in Outbreaks of Foodborne and Zoonotic Origin in the USA in 2006–2017. Microorganisms 2024, 12, 1563. [Google Scholar] [CrossRef]
  38. Wallace, R.L.; Cribb, D.M.; Bulach, D.M.; Ingle, D.J.; Joensen, K.G.; Nielsen, E.M.; Leekitcharoenphon, P.; Stingl, K.; Kirk, M.D. Campylobacter Jejuni ST50, a Pathogen of Global Importance: A Comparative Genomic Analysis of Isolates from Australia, Europe and North America. Zoonoses Public Health 2021, 68, 638–649. [Google Scholar] [CrossRef]
  39. Jin, Y.; Ning, X.; Gao, Y.; Li, W.; Li, Y.; Wang, Y.; Zhou, J.; Stanford, K.; Ba, X.; Jin, Y.; et al. Clostridium Perfringens in the Intestine: Innocent Bystander or Serious Threat? Microorganisms 2024, 12, 1610. [Google Scholar] [CrossRef]
  40. Harrington, W.N.; Kackos, C.M.; Webby, R.J. The Evolution and Future of Influenza Pandemic Preparedness. Exp. Mol. Med. 2021, 53, 737–749. [Google Scholar] [CrossRef]
  41. Jackson, B.R.; Tarr, C.; Strain, E.; Jackson, K.A.; Conrad, A.; Carleton, H.; Katz, L.S.; Stroika, S.; Gould, L.H.; Mody, R.K.; et al. Implementation of Nationwide Real-Time Whole-Genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation. Clin. Infect. Dis. 2016, 63, 380. [Google Scholar] [CrossRef]
  42. Pompon, J.; Morales-Vargas, R.; Manuel, M.; Tan, C.H.; Vial, T.; Tan, J.H.; Sessions, O.M.; Vasconcelos, P.D.C.; Ng, L.C.; Missé, D. A Zika Virus from America Is More Efficiently Transmitted than an Asian Virus by Aedes aegypti Mosquitoes from Asia. Sci. Rep. 2017, 7, 1215. [Google Scholar] [CrossRef] [PubMed]
  43. Lavania, M.; Sharma, V.; Meena, V.K.; Joshi, M.; Potdar, V.; Vipat, V.; Walimbe, A.; Waghchaure, R.; Umare, P.; Vishwanathan, R.; et al. Norovirus Genomes Detected from the Guillain–Barré Syndrome (GBS) Cases in a Community Outbreak in Pune, India, 2025. J. Infect. 2025, 91. [Google Scholar] [CrossRef]
  44. Jara, M.; Frias-De-Diego, A.; Dellicour, S.; Baele, G.; Machado, G. Tracing Foot-and-Mouth Disease Virus Phylogeographical Patterns and Transmission Dynamics. bioRxiv 2019, 590612. [Google Scholar] [CrossRef]
  45. Ferdous, M.; Zhou, K.; De Boer, R.F.; Friedrich, A.W.; Kooistra-Smid, A.M.D.; Rossen, J.W.A. Comprehensive Characterization of Escherichia coli O104: H4 Isolated from Patients in the Netherlands. Front. Microbiol. 2015, 6, 1348. [Google Scholar] [CrossRef] [PubMed]
  46. Kossow, A.; Zhang, W.; Bielaszewska, M.; Rhode, S.; Hansen, K.; Fruth, A.; Rüter, C.; Karch, H.; Mellmann, A. Molecular Characterization of Human Atypical Sorbitol-Fermenting Enteropathogenic Escherichia Coli O157 Reveals High Diversity. J. Clin. Microbiol. 2016, 54, 1357–1363. [Google Scholar] [CrossRef]
  47. Phadungsombat, J.; Imad, H.; Rahman, M.; Nakayama, E.E.; Kludkleeb, S.; Ponam, T.; Rahim, R.; Hasan, A.; Poltep, K.; Yamanaka, A.; et al. A Novel Sub-Lineage of Chikungunya Virus East/Central/South African Genotype Indian Ocean Lineage Caused Sequential Outbreaks in Bangladesh and Thailand. Viruses 2020, 12, 1319. [Google Scholar] [CrossRef]
  48. Deeba, F.; Haider, M.S.H.; Ahmed, A.; Tazeen, A.; Faizan, M.I.; Salam, N.; Hussain, T.; Alamery, S.F.; Parveen, S. Global Transmission and Evolutionary Dynamics of the Chikungunya Virus. Epidemiol. Infect. 2020, 148, e63. [Google Scholar] [CrossRef]
  49. Ramphal, Y.; Tegally, H.; San, J.E.; Reichmuth, M.L.; Hofstra, M.; Wilkinson, E.; Baxter, C.; de Oliveira, T.; Moir, M. Understanding the Transmission Dynamics of the Chikungunya Virus in Africa. Pathogens 2024, 13, 605. [Google Scholar] [CrossRef]
  50. Yang, J.; Zhang, Z.; Wu, Q.; Ding, X.; Yin, C.; Yang, E.; Sun, D.; Wang, W.; Yang, Y.; Guo, F. Multiple Responses Optimization of Antioxidative Components Extracted from Fenugreek Seeds Using Response Surface Methodology to Identify Their Chemical Compositions. Food Sci. Nutr. 2022, 10, 3475–3484. [Google Scholar] [CrossRef]
  51. Ahuir-Baraja, A.E.; Cibot, F.; Llobat, L.; Garijo, M.M. Anthelmintic Resistance: Is a Solution Possible? Exp. Parasitol. 2021, 230, 108169. [Google Scholar] [CrossRef]
  52. Papp, M.; Solymosi, N. Review and Comparison of Antimicrobial Resistance Gene Databases. Antibiotics 2022, 11, 339. [Google Scholar] [CrossRef]
  53. Grad, Y.H.; Harris, S.R.; Kirkcaldy, R.D.; Green, A.G.; Marks, D.S.; Bentley, S.D.; Trees, D.; Lipsitch, M. Genomic Epidemiology of Gonococcal Resistance to Extended-Spectrum Cephalosporins, Macrolides, and Fluoroquinolones in the United States, 2000–2013. J. Infect. Dis. 2016, 214, 1579–1587. [Google Scholar] [CrossRef]
  54. Hussein, R.A.; Al-Ouqaili, M.T.S.; Majeed, Y.H. Detection of Clarithromycin Resistance and 23SrRNA Point Mutations in Clinical Isolates of Helicobacter Pylori Isolates: Phenotypic and Molecular Methods. Saudi J. Biol. Sci. 2022, 29, 513–520. [Google Scholar] [CrossRef]
  55. Zhu, T.; Liu, S.; Ying, Y.; Xu, L.; Liu, Y.; Jin, J.; Ying, J.; Lu, J.; Lin, X.; Li, K.; et al. Genomic and Functional Characterization of Fecal Sample Strains of Proteus Cibarius Carrying Two FloR Antibiotic Resistance Genes and a Multiresistance Plasmid-Encoded Cfr Gene. Comp. Immunol. Microbiol. Infect. Dis. 2020, 69, 101427. [Google Scholar] [CrossRef]
  56. Wu, C.; Zhang, X.; Liang, J.; Li, Q.; Lin, H.; Lin, C.; Liu, H.; Zhou, D.; Lu, W.; Sun, Z.; et al. Characterization of Florfenicol Resistance Genes in the Coagulase-Negative Staphylococcus (CoNS) Isolates and Genomic Features of a Multidrug-Resistant Staphylococcus Lentus Strain H29. Antimicrob. Resist. Infect. Control 2021, 10, 9. [Google Scholar] [CrossRef] [PubMed]
  57. Zhang, X.; Zhu, R.; Li, W.; Ma, J.; Lin, H. Genomic Insights into the Antibiotic Resistance Pattern of the Tetracycline-Degrading Bacterium, Arthrobacter nicotianae OTC-16. Sci. Rep. 2021, 11, 15638. [Google Scholar] [CrossRef]
  58. Boiko, I.; Golparian, D.; Jacobsson, S.; Krynytska, I.; Frankenberg, A.; Shevchenko, T.; Unemo, M. Genomic Epidemiology and Antimicrobial Resistance Determinants of Neisseria gonorrhoeae Isolates from Ukraine, 2013–2018. APMIS 2020, 128, 465–475. [Google Scholar] [CrossRef] [PubMed]
  59. Pelleau, S.; Moss, E.L.; Dhingra, S.K.; Volney, B.; Casteras, J.; Gabryszewski, S.J.; Volkman, S.K.; Wirth, D.F.; Legrand, E.; Fidock, D.A.; et al. Adaptive Evolution of Malaria Parasites in French Guiana: Reversal of Chloroquine Resistance by Acquisition of a Mutation in Pfcrt. Proc. Natl. Acad. Sci. USA 2015, 112, 11672–11677. [Google Scholar] [CrossRef] [PubMed]
  60. Wasakul, V.; Disratthakit, A.; Mayxay, M.; Chindavongsa, K.; Sengsavath, V.; Thuy-Nhien, N.; Pearson, R.D.; Phalivong, S.; Xayvanghang, S.; Maude, R.J.; et al. Malaria Outbreak in Laos Driven by a Selective Sweep for Plasmodium Falciparum Kelch13 R539T Mutants: A Genetic Epidemiology Analysis. Lancet Infect. Dis. 2023, 23, 568–577. [Google Scholar] [CrossRef]
  61. Pearson, R.D.; Amato, R.; Kwiatkowski, D.P. An Open Dataset of Plasmodium falciparum Genome Variation in 7,000 Worldwide Samples. Wellcome Open Res. 2019, 6, 42. [Google Scholar] [CrossRef]
  62. Lee, T.; Pang, S.; Stegger, M.; Sahibzada, S.; Abraham, S.; Daley, D.; Coombs, G. A Three-Year Whole Genome Sequencing Perspective of Enterococcus Faecium Sepsis in Australia. PLoS ONE 2020, 15, e0228781. [Google Scholar] [CrossRef]
  63. de Souza, J.; D’Espindula, H.R.S.; Ribeiro, I.D.F.; Gonçalves, G.A.; Pillonetto, M.; Faoro, H. Carbapenem Resistance in Acinetobacter baumannii: Mechanisms, Therapeutics, and Innovations. Microorganisms 2025, 13, 1501. [Google Scholar] [CrossRef]
  64. Mohammadpour, D.; Memar, M.Y.; Leylabadlo, H.E.; Ghotaslou, A.; Ghotaslou, R. Carbapenem-Resistant Klebsiella Pneumoniae: A Comprehensive Review of Phenotypic and Genotypic Methods for Detection. Microbe 2025, 6, 100246. [Google Scholar] [CrossRef]
  65. Aung, H.L.; Chaidir, L.; Pitaloka, D.A.E.; Miyahara, Y.; Kumar, N.; Soeroto, A.Y.; Cook, G.M.; van Crevel, R.; Alisjahbana, B.; Peacock, S.J.; et al. Whole Genome Sequencing Reveals Novel Resistance-Conferring Mutations and Large Genome Deletions in Drug-Resistant Mycobacterium Tuberculosis Isolates from Indonesia. J. Glob. Antimicrob. Resist. 2025, 44, 314–318. [Google Scholar] [CrossRef]
  66. Brown, A.C.; Chen, J.C.; Francois Watkins, L.K.; Campbell, D.; Folster, J.P.; Tate, H.; Wasilenko, J.; Van Tubbergen, C.; Friedman, C.R. CTX-M-65 Extended-Spectrum β-Lactamase–Producing Salmonella Enterica Serotype Infantis, United States. Emerg. Infect. Dis. 2018, 24, 2284–2291. [Google Scholar] [CrossRef]
  67. Deng, S.; Li, C.; Zhang, H.; Xie, Y.; Wang, X.; Luo, W.; Chen, Z.; Tang, F. Analyzing Shigella in Wuhan: Serotypes, Antimicrobial Resistance, and Public Health Implications. Infect. Drug Resist. 2025, 18, 3745–3760. [Google Scholar] [CrossRef] [PubMed]
  68. Ramatla, T.; Nkhebenyane, J.; Lekota, K.E.; Thekisoe, O.; Monyama, M.; Achilonu, C.C.; Khasapane, G. Global Prevalence and Antibiotic Resistance Profiles of Carbapenem-Resistant Pseudomonas Aeruginosa Reported from 2014 to 2024: A Systematic Review and Meta-Analysis. Front. Microbiol. 2025, 16, 1599070. [Google Scholar] [CrossRef] [PubMed]
  69. Fahran, D.M.; Al-Saadi, B.Q.H. Molecular Detection of Antimicrobial Resistant Genes (ErmB, Mef and TetM) in Streptococcus Pneumoniae in Baghdad Hospital. Biochem. Cell Arch. 2022, 3477–3484. [Google Scholar] [CrossRef]
  70. Bolourchi, N.; Noori Goodarzi, N.; Giske, C.G.; Nematzadeh, S.; Haririzadeh Jouriani, F.; Solgi, H.; Badmasti, F. Comprehensive Pan-Genomic, Resistome and Virulome Analysis of Clinical OXA-48 Producing Carbapenem-Resistant Serratia Marcescens Strains. Gene 2022, 822, 146355. [Google Scholar] [CrossRef] [PubMed]
  71. McGowan, C.R.; Takahashi, E.; Romig, L.; Bertram, K.; Kadir, A.; Cummings, R.; Cardinal, L.J. Community-Based Surveillance of Infectious Diseases: A Systematic Review of Drivers of Success. BMJ Glob. Health 2022, 7, e009934. [Google Scholar] [CrossRef]
  72. Koks, S.; Williams, R.W.; Quinn, J.; Farzaneh, F.; Conran, N.; Tsai, S.J.; Awandare, G.; Goodman, S.R. COVID-19: Time for Precision Epidemiology. Exp. Biol. Med. 2020, 245, 677. [Google Scholar] [CrossRef]
  73. Nigeria Centre for Disease Control (NCDC). Federal Ministry of Health Technical Guidelines for Integrated Disease Surveillance and Response in Nigeria; Nigeria Centre for Disease Control (NCDC): Abuja, Nigeria, 2019. [Google Scholar]
  74. Colebunders, R.L. Control of Communicable Diseases Manual, 19th Edition Control of Communicable Diseases Manual, 19th Edition Edited by David L. Heymann Washington, DC: American Public Health Association, 2008. 746 Pp. $45.00 (Hardcover). Clin. Infect. Dis. 2009, 49, 1292–1293. [Google Scholar] [CrossRef]
  75. Bottichio, L.; Keaton, A.; Thomas, D.; Fulton, T.; Tiffany, A.; Frick, A.; Mattioli, M.; Kahler, A.; Murphy, J.; Otto, M.; et al. Shiga Toxin–Producing Escherichia Coli Infections Associated With Romaine Lettuce—United States, 2018. Clin. Infect. Dis. 2020, 71, e323–e330. [Google Scholar] [CrossRef]
  76. Park, C.J.; Li, J.; Zhang, X.; Gao, F.; Benton, C.S.; Andam, C.P. Diverse Lineages of Multidrug Resistant Clinical Salmonella Enterica and a Cryptic Outbreak in New Hampshire, USA Revealed from a Year-Long Genomic Surveillance. Infect. Genet. Evol. 2021, 87, 104645. [Google Scholar] [CrossRef]
  77. Feldgarden, M.; Brover, V.; Haft, D.H.; Prasad, A.B.; Slotta, D.J.; Tolstoy, I.; Tyson, G.H.; Zhao, S.; Hsu, C.H.; McDermott, P.F.; et al. Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates. Antimicrob. Agents Chemother. 2019, 63, e00483-19. [Google Scholar] [CrossRef]
  78. Rokney, A.; Valinsky, L.; Vranckx, K.; Feldman, N.; Agmon, V.; Moran-Gilad, J.; Weinberger, M. WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter Jejuni Isolates From Israel. Front. Cell Infect. Microbiol. 2020, 10, 365. [Google Scholar] [CrossRef]
  79. Paranthaman, K.; Mook, P.; Curtis, D.; Evans, E.W.; Crawley-Boevey, E.; Dabke, G.; Carroll, K.; McCormick, J.; Dallman, T.J.; Crook, P. Development and Evaluation of an Outbreak Surveillance System Integrating Whole Genome Sequencing Data for Non-Typhoidal Salmonella in London and South East of England, 2016-17. Epidemiol Infect 2021, 149, e164. [Google Scholar] [CrossRef] [PubMed]
  80. Karlsson, M.; Lutgring, J.D.; Ansari, U.; Lawsin, A.; Albrecht, V.; McAllister, G.; Daniels, J.; Lonsway, D.; McKay, S.; Beldavs, Z.; et al. Molecular Characterization of Carbapenem-Resistant Enterobacterales Collected in the United States. Microb. Drug Resist. 2022, 28, 389–397. [Google Scholar] [CrossRef] [PubMed]
  81. Corman, V.M.; Landt, O.; Kaiser, M.; Molenkamp, R.; Meijer, A.; Chu, D.K.W.; Bleicker, T.; Brünink, S.; Schneider, J.; Schmidt, M.L.; et al. Detection of 2019 Novel Coronavirus (2019-NCoV) by Real-Time RT-PCR. Eurosurveillance 2020, 25. [Google Scholar] [CrossRef] [PubMed]
  82. Chen, C.; Nadeau, S.; Yared, M.; Voinov, P.; Xie, N.; Roemer, C.; Stadler, T. CoV-Spectrum: Analysis of Globally Shared SARS-CoV-2 Data to Identify and Characterize New Variants. Bioinformatics 2022, 38, 1735–1737. [Google Scholar] [CrossRef]
  83. Kalinich, C.C.; Jensen, C.G.; Neugebauer, P.; Petrone, M.E.; Peña-Hernández, M.; Ott, I.M.; Wyllie, A.L.; Alpert, T.; Vogels, C.B.F.; Fauver, J.R.; et al. Real-Time Public Health Communication of Local SARS-CoV-2 Genomic Epidemiology. PLoS Biol. 2020, 18, e3000869. [Google Scholar] [CrossRef]
  84. Carter, L.L.; Yu, M.A.; Sacks, J.A.; Barnadas, C.; Pereyaslov, D.; Cognat, S.; Briand, S.; Ryan, M.J.; Samaan, G. Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 2022–2032. Bull World Health Organ 2022, 100, 239. [Google Scholar] [CrossRef]
  85. Akande, O.W.; Carter, L.L.; Abubakar, A.; Achilla, R.; Barakat, A.; Gumede, N.; Guseinova, A.; Inbanathan, F.Y.; Kato, M.; Koua, E.; et al. Strengthening Pathogen Genomic Surveillance for Health Emergencies: Insights from the World Health Organization’s Regional Initiatives. Front. Public. Health 2023, 11, 1146730. [Google Scholar] [CrossRef]
  86. Bourgeois, F.T.; Porter, S.C.; Valim, C.; Jackson, T.; Cook, E.F.; Mandl, K.D. The Value of Patient Self-Report for Disease Surveillance. J. Am. Med. Inform. Assoc. 2007, 14, 765–771. [Google Scholar] [CrossRef]
  87. Brownstein, J.S.; Freifeld, C.C.; Madoff, L.C. Digital Disease Detection—Harnessing the Web for Public Health Surveillance. N. Engl. J. Med. 2009, 360, 2153. [Google Scholar] [CrossRef]
  88. Charles-Smith, L.E.; Reynolds, T.L.; Cameron, M.A.; Conway, M.; Lau, E.H.Y.; Olsen, J.M.; Pavlin, J.A.; Shigematsu, M.; Streichert, L.C.; Suda, K.J.; et al. Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. PLoS ONE 2015, 10, e0139701. [Google Scholar] [CrossRef]
  89. Restrepo, J.C.; Dueñas, D.; Corredor, Z.; Liscano, Y. Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment. Cancers 2023, 15, 3474. [Google Scholar] [CrossRef] [PubMed]
  90. Hulsen, T.; Jamuar, S.S.; Moody, A.R.; Karnes, J.H.; Varga, O.; Hedensted, S.; Spreafico, R.; Hafler, D.A.; McKinney, E.F. From Big Data to Precision Medicine. Front. Med. 2019, 6, 34. [Google Scholar] [CrossRef] [PubMed]
  91. Behl, A.; Nair, A.; Mohagaonkar, S.; Yadav, P.; Gambhir, K.; Tyagi, N.; Sharma, R.K.; Butola, B.S.; Sharma, N. Threat, Challenges, and Preparedness for Future Pandemics: A Descriptive Review of Phylogenetic Analysis Based Predictions. Infect. Genet. Evol. 2022, 98, 105217. [Google Scholar] [CrossRef] [PubMed]
  92. Forster, P.; Forster, L.; Renfrew, C.; Forster, M. Phylogenetic Network Analysis of SARS-CoV-2 Genomes. Proc. Natl. Acad. Sci. USA 2020, 117, 9241–9243. [Google Scholar] [CrossRef] [PubMed]
  93. Yebra, G.; Ragonnet-Cronin, M.; Ssemwanga, D.; Parry, C.M.; Logue, C.H.; Cane, P.A.; Kaleebu, P.; Leigh Brown, A.J. Analysis of the History and Spread of HIV-1 in Uganda Using Phylodynamics. J. Gen. Virol. 2015, 96, 1890. [Google Scholar] [CrossRef]
  94. Brito, A.F.; Semenova, E.; Dudas, G.; Hassler, G.W.; Kalinich, C.C.; Kraemer, M.U.G.; Ho, J.; Tegally, H.; Githinji, G.; Agoti, C.N.; et al. Global Disparities in SARS-CoV-2 Genomic Surveillance. Nat. Commun. 2022, 13, 7003. [Google Scholar] [CrossRef] [PubMed]
  95. Hall, M.D.; Woolhouse, M.E.J.; Rambaut, A. The Effects of Sampling Strategy on the Quality of Reconstruction of Viral Population Dynamics Using Bayesian Skyline Family Coalescent Methods: A Simulation Study. Virus Evol. 2016, 2, vew003. [Google Scholar] [CrossRef]
  96. Dellicour, S.; Hong, S.L.; Vrancken, B.; Chaillon, A.; Gill, M.S.; Maurano, M.T.; Ramaswami, S.; Zappile, P.; Marier, C.; Harkins, G.W.; et al. Dispersal Dynamics of SARS-CoV-2 Lineages during the First Epidemic Wave in New York City. PLoS Pathog. 2021, 17, e1009571. [Google Scholar] [CrossRef]
  97. Lemey, P.; Hong, S.L.; Hill, V.; Baele, G.; Poletto, C.; Colizza, V.; O’Toole, Á.; McCrone, J.T.; Andersen, K.G.; Worobey, M.; et al. Accommodating Individual Travel History and Unsampled Diversity in Bayesian Phylogeographic Inference of SARS-CoV-2. Nat. Commun. 2020, 11, 5110. [Google Scholar] [CrossRef] [PubMed]
  98. Bull, R.A.; Adikari, T.N.; Ferguson, J.M.; Hammond, J.M.; Stevanovski, I.; Beukers, A.G.; Naing, Z.; Yeang, M.; Verich, A.; Gamaarachchi, H.; et al. Analytical Validity of Nanopore Sequencing for Rapid SARS-CoV-2 Genome Analysis. Nat. Commun. 2020, 11, 6272. [Google Scholar] [CrossRef]
  99. Tyson, J.R.; James, P.; Stoddart, D.; Sparks, N.; Wickenhagen, A.; Hall, G.; Choi, J.H.; Lapointe, H.; Kamelian, K.; Smith, A.D.; et al. Improvements to the ARTIC Multiplex PCR Method for SARS-CoV-2 Genome Sequencing Using Nanopore. bioRxiv 2020. [Google Scholar] [CrossRef]
  100. Didelot, X.; Maiden, M.C.J. Impact of Recombination on Bacterial Evolution. Trends Microbiol. 2010, 18, 315–322. [Google Scholar] [CrossRef]
  101. Lythgoe, K.A.; Hall, M.; Ferretti, L.; de Cesare, M.; MacIntyre-Cockett, G.; Trebes, A.; Andersson, M.; Otecko, N.; Wise, E.L.; Moore, N.; et al. SARS-CoV-2 within-Host Diversity and Transmission. Science 2021, 372, eabg0821. [Google Scholar] [CrossRef]
  102. Quince, C.; Walker, A.W.; Simpson, J.T.; Loman, N.J.; Segata, N. Shotgun Metagenomics, from Sampling to Analysis. Nat. Biotechnol. 2017, 35, 833–844. [Google Scholar] [CrossRef]
  103. Schnell, I.B.; Bohmann, K.; Gilbert, M.T.P. Tag Jumps Illuminated--Reducing Sequence-to-Sample Misidentifications in Metabarcoding Studies. Mol. Ecol. Resour. 2015, 15, 1289–1303. [Google Scholar] [CrossRef]
  104. Erlich, Y.; Narayanan, A. Routes for Breaching and Protecting Genetic Privacy. Nat. Rev. Genet. 2014, 15, 409–421. [Google Scholar] [CrossRef]
  105. Volz, E.M.; Siveroni, I. Bayesian Phylodynamic Inference with Complex Models. PLoS Comput. Biol. 2018, 14, e1006546. [Google Scholar] [CrossRef]
  106. WHO. WHO SARS-CoV-2 Genomic Sequencing for Public Health Goals: Interim Guidance; WHO: Geneva, Switzerland, 2021. [Google Scholar]
  107. Turakhia, Y.; Thornlow, B.; Hinrichs, A.S.; De Maio, N.; Gozashti, L.; Lanfear, R.; Haussler, D.; Corbett-Detig, R. Ultrafast Sample Placement on Existing TRees (UShER) Enables Real-Time Phylogenetics for the SARS-CoV-2 Pandemic. Nat. Genet. 2021, 53, 809–816. [Google Scholar] [CrossRef] [PubMed]
  108. Da Silva Filipe, A.; Shepherd, J.G.; Williams, T.; Hughes, J.; Aranday-Cortes, E.; Asamaphan, P.; Ashraf, S.; Balcazar, C.; Brunker, K.; Campbell, A.; et al. Genomic Epidemiology Reveals Multiple Introductions of SARS-CoV-2 from Mainland Europe into Scotland. Nat. Microbiol. 2020, 6, 112–122. [Google Scholar] [CrossRef]
  109. COVID-19 Genomics UK (COG-UK) Consortium. An Integrated National Scale SARS-CoV-2 Genomic Surveillance Network. Lancet Microbe 2020, 1, e99–e100. [Google Scholar] [CrossRef] [PubMed]
  110. Munnink, B.B.O.; Sikkema, R.S.; Nieuwenhuijse, D.F.; Molenaar, R.J.; Munger, E.; Molenkamp, R.; Van Der Spek, A.; Tolsma, P.; Rietveld, A.; Brouwer, M.; et al. Transmission of SARS-CoV-2 on Mink Farms between Humans and Mink and Back to Humans. Science 2020, 371, 172. [Google Scholar] [CrossRef] [PubMed]
  111. FAO. One Health Joint Plan of Action, 2022–2026; FAO: Rome, Italy, 2022. [Google Scholar] [CrossRef]
  112. Lytras, S.; Lamb, K.D.; Ito, J.; Grove, J.; Yuan, K.; Sato, K.; Hughes, J.; Robertson, D.L. Pathogen Genomic Surveillance and the AI Revolution. J. Virol. 2025, 99, e01601-24. [Google Scholar] [CrossRef]
Table 1. Applications of Whole-Genome Sequencing (WGS) in Disease Surveillance and Outbreak Investigation.
Table 1. Applications of Whole-Genome Sequencing (WGS) in Disease Surveillance and Outbreak Investigation.
PathogenRegion/ContextKey WGS FindingsPublic Health ImpactReference
Plasmodium vivaxChina–Myanmar border, Greater Mekong SubregionDistinct local genetic clade identified; clustering confirmed by ML and IBD analysesInforms geographically targeted malaria elimination strategies; highlights risk of parasite resurgence[20]
Dengue virus (DENV-1–4)Nepal; Asia, Africa, Americas, EuropeGenomes linked to strains from India (2019) and China (2018); global movement detectedTraces outbreak sources; supports control strategies in endemic and newly affected regions[28]
West Nile virus (WNV)Europe and North America≥13 introductions in Europe; lineage 2 expanding into temperate regionsImproves understanding of viral evolution and spread; informs surveillance priorities[32]
Methicillin-resistant Staphylococcus aureus (MRSA)Asia, Africa, Middle East, EuropeTravel-associated transmission detected; genomic tools outperform traditional typingSupports infection control policies and strengthens cross-border AMR surveillance[33]
Candida aurisIndiaClonal strains with low genetic diversity; multidrug resistance linked to transporter genesIdentifies antifungal resistance mechanisms; guides hospital infection control measures[34]
Escherichia coli ST131Global (e.g., Europe, Asia, North America)Dominant clone associated with fluoroquinolone resistance; rapid global spreadInforms AMR surveillance and control strategies[35]
Vibrio parahaemolyticusAsia; Gulf of MexicoIdentified oceanic gene pools; frequent recombination and host adaptationEnhances understanding of environmental reservoirs and transmission dynamics[36]
Salmonella entericaUnited States Emergence of epidemic strains; network-based genomic analysisImproves outbreak detection and source attribution in foodborne illnesses[37]
Campylobacter jejuniAustralia, Europe, North AmericaEvidence of rapid host switching; challenges in source attributionGuides public health interventions and food safety measures[38]
Clostridium perfringensGlobalHigh genetic diversity; identification of enterotoxin-producing strainsAssists in food safety and public health responses to foodborne outbreaks[39]
Influenza virusGlobalDynamic genome evolution; identification of novel circulating strainsSupports vaccine development and pandemic preparedness strategies[40]
Listeria monocytogenesUnited StatesIdentification of outbreak sources; improved traceback capabilityStrengthens food safety measures and outbreak response strategies[41]
Zika virusAmericas, Southeast AsiaTraced spread via mosquito vectors; identified mutations linked to microcephalyInforms vector control strategies and public health responses[42]
NorovirusIndia (Pune)Mutated GII.16[P16] strain linked to Guillain–Barré syndrome outbreakHighlights need for enhanced surveillance of neurological complications[43]
Foot-and-mouth disease virus (FMDV)AsiaRevealed within-host viral diversity; identified mutations linked to tissue adaptationImproves understanding of viral evolution and transmission dynamics[44]
Table 2. Selected Case Studies of WGS in Identifying Antimicrobial Resistance Determinants.
Table 2. Selected Case Studies of WGS in Identifying Antimicrobial Resistance Determinants.
PathogenKey Resistance Genes/MutationsDrug ResistanceWGS ContributionReference
Neisseria gonorrhoeaemtrR mutations, penA allelesAzithromycin, cefiximeIdentified novel mutations associated with treatment failure[53]
Helicobacter pylori23S rRNA mutationsClarithromycinLinked specific mutations to macrolide resistance[54]
Proteus spp. floR (plasmid + chromosome)FlorfenicolHighlighted role of mobile genetic elements in gene transfer[55]
Staphylococcus lentus11 resistance genes (plasmid + chromosome)Multidrug resistanceDefined genomic basis of MDR[56]
Arthrobacter nicotianae8 resistance genes, mobile loci outside plasmidsTetracyclineIdentified extrachromosomal AMR genes[57]
Neisseria gonorrhoeae (Ukraine isolates)GyrA S91F, ParC S87R, rpsJ V57M, tetM, penA-34.001Ciprofloxacin, tetracycline, β-lactamsPhylogenomic analysis revealed MDR lineages and key mutations[58]
Plasmodium falciparumpfcrt K76T, C350R, pfaat1 S258L, pfdhfr, pfdhps, pfk13, pfmdr1, pfama1Chloroquine, artemisinin, multidrug resistanceGWAS + WGS identified novel molecular markers for surveillance[60]
Enterococcus faeciumvanA, vanB operonsVancomycinWGS of 1025 isolates defined clonal clusters, geographic subclusters, and resistance determinants[62]
Acinetobacter baumanniiOXA-23, OXA-24, OXA-58 carbapenemasesCarbapenemsCharacterized global spread and clonal expansion of resistant strains[63]
Klebsiella pneumoniaeKPC, NDM, OXA-48 carbapenemasesCarbapenemsIdentified hypervirulent strains with multidrug resistance[64]
Mycobacterium tuberculosisrpoB mutations, katG mutationsRifampicin, isoniazidWhole-genome sequencing for rapid detection of resistance[65]
Salmonella entericablaCTX-M, blaTEM, blaSHVExtended-spectrum cephalosporinsTracked spread of resistance genes in foodborne outbreaks[66]
Shigella spp.blaCTX-M, aac(3)-IVAminoglycosides, cephalosporinsIdentified plasmid-mediated resistance mechanisms[67]
Pseudomonas aeruginosaVIM, IMP, NDM metallo-β-lactamasesCarbapenemsCharacterized resistance profiles and clonal spread[68]
Streptococcus pneumoniaeermB, mefAMacrolidesIdentified genetic determinants of macrolide resistance[69]
EnterobacteriaceaeblaNDM, blaKPC, blaOXA-48CarbapenemsWhole-genome sequencing for surveillance of resistant strains[70]
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Tiwari, S.; Dhakal, T.; Kim, B.-J.; Jang, G.S.; Oh, Y. Genomics in Epidemiology and Disease Surveillance: An Exploratory Analysis. Life 2025, 15, 1848. https://doi.org/10.3390/life15121848

AMA Style

Tiwari S, Dhakal T, Kim B-J, Jang GS, Oh Y. Genomics in Epidemiology and Disease Surveillance: An Exploratory Analysis. Life. 2025; 15(12):1848. https://doi.org/10.3390/life15121848

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Tiwari, Shraddha, Thakur Dhakal, Baek-Jun Kim, Gab Sue Jang, and Yeonsu Oh. 2025. "Genomics in Epidemiology and Disease Surveillance: An Exploratory Analysis" Life 15, no. 12: 1848. https://doi.org/10.3390/life15121848

APA Style

Tiwari, S., Dhakal, T., Kim, B.-J., Jang, G. S., & Oh, Y. (2025). Genomics in Epidemiology and Disease Surveillance: An Exploratory Analysis. Life, 15(12), 1848. https://doi.org/10.3390/life15121848

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