One Health diagnostics at the human–animal–environment interface: a structured narrative review of cross-sector collaboration, capacity building, and technological innovations

1 Introduction

Many pathogens circulate across interconnected human, animal, and environmental systems, making zoonotic and emerging infectious diseases major global health threats. More than 200 zoonotic diseases have been reported, and zoonoses constitute a substantial proportion of human infectious diseases. Antimicrobial resistance, which can be transmitted across species and environments via multiple pathways, exacerbates these risks and complicates the prevention and treatment of infectious diseases (1). Emerging and re-emerging zoonoses also have substantial socioeconomic impacts, including human morbidity and mortality, animal disease, decreased livestock productivity, economic losses, and pressure on national health security systems (2). Wildlife reservoirs, domestic animals, contaminated environments, unsafe food systems, and poor sanitation sustain and transmit zoonotic pathogens, whereas habitat degradation, wildlife trafficking, rural encroachment, and climate change may increase human–wildlife contact and spillover risks (3). Infectious disease emergence and spread are also driven by broader factors, including urbanization, land-use change, deforestation, agricultural intensification, extreme weather events, and transnational movement of people (4). Consequently, prevention, preparedness, and prompt outbreak control would benefit greatly from early detection and coordinated One Health surveillance and response systems (5).

The One Health framework is an example of a practical, integrated approach to infectious disease detection and response that recognizes the interdependence of human health, animal health, and environmental conditions. Unlike the traditional initial diagnostic method, which is based solely on clinical disease recognition in humans after disease emergence, One Health surveillance integrates data from humans, animals, and the environment to identify risk at an earlier stage, enabling timely intervention. One Health is defined by Singh et al. (6) as a cross-disciplinary, multisectoral, integrated approach that links human and animal health surveillance with environmental health surveillance to better understand the emergence, transmission, and persistence of infectious diseases. In this framework, public health surveillance provides information on human disease trends; veterinary surveillance detects animal reservoirs, human spillover risks, and sentinel animal events; and environmental surveillance identifies ecological and climatic factors that may influence pathogen circulation. Furthermore, Olsen (7) highlights the importance of veterinary medicine in public health through disease monitoring, reporting, and biosafety practices on farms, in slaughterhouses, and at animal-product processing facilities. Data from Brazil suggest that merging sector-specific field teams, leveraging an electronic data system, and training a surveillance workforce can enhance early warning and response capacity within a One Health framework (8). Similarly, multisectoral prioritization in Malawi exemplifies the need for coordinated governance, joint outbreak investigation, laboratory strengthening, surveillance planning, and coordinated preparedness activities to respond effectively to zoonotic threats at the human–animal–environment interface (9).

The recent collaboration among the Quadripartite Partnership of the World Health Organization (WHO), Food and Agriculture Organization (FAO), United Nations Environment Program (UNEP), and World Organization for Animal Health (WOAH) has strengthened efforts to operationalize One Health. The One Health Joint Plan of Action (2022–2026) also promotes multisectoral integration through global and country-level coordination, surveillance, capacity building, and preparedness across the human, animal, plant, and environmental health sectors (10). National-level One Health capacities, identification of implementation gaps, and promotion of evidence-based policy development are best evaluated through complementary frameworks, including the Theory of Change for One Health and the One Health Assessment Tool (11, 12). These frameworks provide critical directions for integrated surveillance systems, antimicrobial resistance monitoring, laboratory strengthening, and cross-sector collaboration (13).

One Health diagnostics encompasses a wide, multi-sectoral, integrated approach to the detection, monitoring, reporting, and response to infectious disease threats across the human–animal–environment interface. This goes beyond laboratory testing to encompass pathogen detection, laboratory networks, field surveillance, environmental monitoring, genomic surveillance, data systems, and reporting systems. Trinh et al. (14) argue that One Health approaches should be based on ecological relationships among human, animal, and environmental microbiomes, advocating diagnostic perspectives that examine microbial transfer and ecosystem interactions rather than merely raising alarms about isolated pathogens. Antunes et al. (15) focused on the importance of veterinary microbiology, molecular diagnostics, environmental surveillance, laboratory networks, and data-sharing systems in combating emerging infectious diseases. Thus, One Health diagnostics encompass both traditional and advanced methods, such as culture-based testing, antimicrobial susceptibility testing, polymerase chain reaction (PCR), sequencing, metagenomics, and artificial intelligence (AI)-supported tools (16). Integrated diagnostic solutions provide early warning, outbreak confirmation, and monitoring of antimicrobial resistance, food safety, and pandemic preparedness (5).

One Health diagnostics encompass the technical capacity for pathogen detection, outbreak confirmation, antimicrobial resistance monitoring, genomic surveillance, and cross-sector reporting, provided primarily by veterinary and public health laboratories. Under the One Health surveillance framework, Tast Lahti et al. (17) demonstrated that public health, animal health, and food safety laboratories can coordinate to detect, characterize, and report foodborne pathogens while acknowledging that variations in testing practices and notification requirements may complicate cross-sector interpretation. Laboratory data networks are also important for antimicrobial resistance surveillance, as illustrated by AMRNet in Canada, which integrates clinical, public health, and veterinary laboratory data to provide routine resistance monitoring, data-driven investigations, and potential genomic surveillance (18). Implementing a One Health laboratory system requires robust sample referral pathways, a trained workforce, quality assurance, and biosafety measures, coupled with interoperable reporting systems. Additional evidence from Nigeria and Malawi indicates that low-resource settings could particularly benefit from workforce development, the establishment of digital information-sharing systems, the strengthening of laboratory infrastructure, and enhanced diagnostic capacity to improve zoonotic disease surveillance (9).

In addition to adequate laboratory capacity, health diagnostics also rely on effective cross-sector collaboration and governance to ensure that surveillance results lead to timely public health action. Zoonotic threats are multisectoral and involve shared responsibilities; hence, strengthened collaboration among public health agencies, veterinary services, environmental authorities, academic institutions, non-governmental organizations (NGOs), and international partners is essential. Pal et al. (19) noted that veterinarians, medical professionals, and environmental scientists offer individual yet complementary skill sets that support the One Health approach. Similarly, international organizations and universities provide important policy-making expertise, training opportunities, research, and coordination. Several recent SIS-OT implementation studies in West Africa and Nigeria, for example, demonstrated that multistakeholder, multisectoral platforms can facilitate collaboration in surveillance coordination, data sharing, and legal harmonization, while also fostering national ownership of One Health activities (20, 21). In this context, governance frameworks are needed to define institutional roles, reporting pathways, accountability mechanisms, and resource allocation. In the absence of such coordination, diagnostic systems may remain poorly integrated, delaying detection, limiting the ability to mount a timely and effective response, and underutilizing laboratory and surveillance resources (22).

Emerging technologies are progressively expanding the operational scope of One Health diagnostics by enhancing pathogen detection and surveillance through improved speed, sensitivity, portability, and analytical value. Molecular tools, such as PCR, multiplex PCR, and CRISPR/Cas-based diagnostics, enable rapid detection of viral and antimicrobial resistance targets in clinical samples and can also support outbreak surveillance and point-of-care testing (23). Genomic approaches, such as next-generation sequencing and metagenomics, enhance pathogen characterization, the detection of resistance genes, and molecular epidemiology in clinical, animal, and environmental samples. Integrating diverse predictors and using digital and analytical tools for risk assessment further enhance One Health surveillance. Machine learning, geographic information systems, and environmental data have also been used to forecast dengue from a One Health perspective (24). In addition, portable biosensors, smartphone-linked platforms, and point-of-care devices can facilitate field-based testing, real-time data sharing, and rapid decision-making in decentralized settings (25).

The value of emerging diagnostic technologies will depend not only on their detection capacity but also on the timeliness of communication, interpretation, and use of diagnostic information across multiple surveillance systems. Interoperable data flows among human health, animal health, environmental monitoring, and laboratories are needed for integrated One Health surveillance. Burdakov et al. (26) described the Electronic Integrated Disease Surveillance System as a platform for collecting, sharing, and analyzing epidemiological, clinical, and laboratory data across the medical, veterinary, and environmental sectors. Likewise, Singh et al. (6) advocated integrating human, animal, and environmental surveillance data to enable the early detection of potential disease threats and rapid response. One example is Canada’s AMRNet, which draws on laboratory-based resistance data from clinical, public health, and veterinary sources to strengthen data-sharing platforms for antimicrobial resistance (AMR) monitoring, risk communication, and the investigation of emerging resistance patterns (18). Numerous barriers remain, including fragmented databases, differing standards, privacy challenges, inadequate digital infrastructure, and institutional silos (27).

These data and interoperability requirements are critical in low-resource settings, where diagnostic capacity is limited by inadequate laboratory infrastructure, shortages of trained healthcare professionals, weak quality assurance systems, fragmented sample referral pathways, and limited access to high-throughput technologies. As reported by Ondoa et al. (28), many African laboratory systems provide limited diagnostic services, have weak governance, lack quality management systems and coherent specimen referral systems, and face shortages of skilled laboratory personnel, all of which can hamper timely disease detection and surveillance performance. These limitations are particularly important for One Health because emerging zoonotic pathogens frequently need to be identified early at local human–animal–environment interfaces, where infrastructure and workforce capacity may be at their weakest (29). Capacity building thus requires concerted efforts to strengthen tiered laboratory networks, workforce training, biosafety, quality management, sample transportation, digital reporting, and sustainable financing. Examples of surveillance- and implementation-capacity strengthening through workforce development in resource-limited settings include the establishment of the One Health Field Epidemiology and Laboratory Training Program in The Gambia and diagnostic connectivity platforms in Uganda (30).

Despite growing attention to One Health, zoonotic diseases, surveillance, and diagnostic technologies, evidence remains scattered across diverse disciplines and implementation contexts. Meanwhile, studies of zoonotic influenza surveillance show that many countries still have limited active and comprehensive surveillance systems at the human–animal interface, while gaps persist in the field validation of innovative diagnostic technologies, One Health data integration, environmental surveillance, and the translation of surveillance findings into risk assessment and policy action (31). Likewise, evidence on emerging zoonotic bacterial infections indicates incomplete surveillance systems, inequitable access to molecular or genomic diagnostics, and ongoing constraints related to cost, infrastructure, and workforce capacity, especially in low- and middle-income countries. Broader reviews of the One Health approach highlight the need for collaboration, workforce training, technology development, policy development, and the implementation of infrastructure improvements across human, animal, and environmental health systems (32). A structured narrative review is appropriate in this context to synthesize how diagnostics, laboratory systems, technologies, data sharing, governance, and capacity building are interconnected within One Health diagnostic systems.

The objective of this structured narrative review is to synthesize current evidence on One Health diagnostics at the human–animal–environment interface, with a specific focus on cross-sector collaboration among the human, animal, and environmental health sectors; veterinary and public health diagnostic laboratory systems; diagnostic capacity building in low-resource settings; emerging diagnostic technologies, including polymerase chain reaction, next-generation sequencing, metagenomics, artificial intelligence, machine learning, geographic information systems, mobile health, biosensors, and point-of-care testing; data sharing, interoperability, and integrated surveillance systems; diagnostic preparedness for emerging zoonotic pathogens; and the policy, governance, and practical implementation of One Health diagnostic systems.

2 Methodology

2.1 Review design

This review was conducted as a structured narrative review to synthesize the literature on One Health diagnostics at the human–animal–environment interface. It aimed to provide a thematic synthesis of the evidence on diagnostic systems, surveillance, pathogen detection, diagnostic preparedness, cross-sector collaborations, capacity strengthening, emerging technologies, data sharing, interoperability, governance, and policy.

2.2 Literature search strategy

A targeted literature search was conducted using Google Scholar to identify articles, reviews, case studies, technical reports, policy documents, and guidelines from various governmental and non-governmental organizations. Google Scholar was used to capture multidisciplinary and gray literature, whereas PubMed and Web of Science were included to enhance coverage of peer-reviewed biomedical, veterinary, environmental, public health, and policy-related literature. The search was restricted to English-language literature published from 2010 to 2026. The search terms were constructed based on the study objective and eligibility criteria. Keywords were combined using Boolean operators and included One Health assessment, surveillance, integrated pathogen detection, zoonotic diseases, new and emerging infectious diseases, antimicrobial resistance, diagnostic preparedness, cross-sector collaboration, data sharing, capacity building, laboratory systems, governance, and emerging technologies. The following search terms and combinations were used: “One Health” AND diagnostics AND surveillance AND zoonotic; “One Health” AND “pathogen detection” AND “emerging infectious diseases”; “One Health” AND “diagnostic preparedness” AND “pandemic preparedness”; “One Health” AND “veterinary diagnostic laboratories” AND surveillance; “One Health” AND “data sharing” AND interoperability; “One Health” AND diagnostics AND “capacity building” AND “low-resource settings”; “One Health” AND diagnostics AND (“next-generation sequencing” OR metagenomics OR “artificial intelligence” OR GIS); “One Health” AND governance AND “integrated surveillance”; “One Health” AND “point-of-care testing” AND zoonotic; and “One Health” AND “multisectoral collaboration” AND surveillance.

2.3 Study selection

Records identified through Google Scholar, PubMed, and Web of Science were screened independently by two reviewers using predefined eligibility criteria. Studies and documents deemed relevant were then evaluated for their potential to contribute to the review’s core themes. Studies were eligible if they addressed One Health diagnostics, surveillance, pathogen detection, diagnostic preparedness, laboratory systems, data sharing, capacity building, technological innovation, governance, or cross-sector implementation. Relevant policy documents, technical reports, and strategic frameworks describing One Health approaches from international organizations, including the WHO, FAO, UNEP, and WOAH, were also considered when they contributed to the conceptual, operational, and policy dimensions of One Health diagnostics, in addition to peer-reviewed literature. Studies were excluded if they were not relevant to One Health, focused exclusively on a single health sector without relevance to the One Health framework, or were not applicable to diagnostics, surveillance, laboratory systems, preparedness, data sharing, or detection technologies. Duplicate records and records lacking sufficient information for relevance assessment were also excluded. Any disagreements between reviewers were resolved through discussion and consensus.

2.4 Eligibility criteria

The literature included studies on One Health diagnostics, surveillance, pathogen detection, and diagnostic preparedness at the human–animal–environment interface. The included studies or documents had to encompass at least two of the three One Health domains: human health, animal health, and environmental health.

Literature was included if it focused on zoonotic diseases, emerging infectious diseases, foodborne pathogens, environmental pathogens, antimicrobial resistance, outbreak preparedness, or pandemic diagnostics. Studies were also eligible if they addressed diagnostic tools, laboratory systems, surveillance systems, data systems, field detection, genomic diagnostics, artificial intelligence, geographic information systems, mobile health, biosensors, point-of-care testing, cross-sector collaboration, multisectoral coordination, international organizations, governance, policy frameworks, integrated surveillance, laboratory strengthening, workforce training, quality assurance, diagnostic access, low-resource settings, low- and middle-income countries (LMICs), or infrastructure development. Eligible document types included original research articles, review articles (including systematic reviews and scoping reviews), case studies, policy documents, technical reports, and organizational guidelines. The literature search criteria were limited to English-language studies published between 2010 and 2026. Studies were excluded if they (i) were not relevant to One Health (i.e., addressed only human, animal, or environmental health without cross-sector relevance); (ii) discussed zoonotic diseases without relevance to diagnostics, surveillance, preparedness, laboratory systems, data sharing, or detection technologies; (iii) focused solely on clinical diagnosis or treatment without relevance to One Health; (iv) evaluated only a diagnostic assay without relevance to surveillance, outbreak detection, field implementation, or One Health systems; (v) focused exclusively on vaccines, therapeutics, or clinical management without relevance to diagnostic preparedness or surveillance; (vi) addressed non-infectious diseases without a clear connection to One Health diagnostic systems; (vii) were published in languages other than English; (viii) were duplicate records; or (ix) lacked sufficient information to assess relevance.

2.5 Data extraction and thematic organization

Data were extracted by one reviewer and independently verified by a second reviewer for accuracy and consistency. This included document type, disease or pathogen focus, One Health domains addressed, relevance to diagnostics or surveillance, technological focus, governance or collaboration components, capacity-building relevance, and relevance to the review themes. Eligible studies were classified into key themes: cross-sector collaboration, veterinary and public health diagnostic laboratory systems, capacity development in low-resource settings, emerging diagnostic technologies, data sharing, interoperability, integrated surveillance, diagnostic preparedness for emerging pathogens, case studies and lessons learned, and future research and policy implications.

2.6 Narrative synthesis

A thematic narrative synthesis method was used to synthesize the selected literature. Due to the heterogeneity of the sources included in this review (original research articles, reviews, case studies, policy documents, technical reports, and organizational guidelines), studies were not pooled statistically. The synthesis examined major findings, common barriers, implementation challenges, technological opportunities, governance issues, and future directions for strengthening One Health diagnostic systems. The review was designed as a structured narrative review (not a systematic review or meta-analysis), and formal risk-of-bias assessment, evidence grading, and quantitative synthesis were not performed.

3 Cross-sector collaboration in One Health

3.1 The role of veterinary diagnostic laboratories

Veterinary diagnostic laboratories (VDLs) contribute to the One Health initiative by supporting the surveillance of zoonotic diseases, thereby contributing to public health (33). In VDLs, high-standard testing is performed to assess the health status of animals, serving as a primary diagnostic tool for zoonotic diseases (34). The San Diego County Disease Diagnostic Laboratory uses integrated VDLs and public health approaches, providing a beneficial model for One Health collaboration (35). VDLs use techniques such as advanced technologies and vaccinations to detect zoonotic diseases at an early stage (36). During the SARS-CoV-2 pandemic, approximately 3.8 million tests were performed by VDLs, demonstrating their contribution to public health (37).

VDLs face several challenges, including resource deficiency, shortage of well-trained personnel, increasing regulatory demands, and infrastructure costs, which affect their contributions to One Health (38). Limited access to human health data and the lack of standardized protocols across the veterinary and public health sectors are also major hurdles (39). Requirements for veterinary testing, quality control materials, and proficiency programs should be incorporated into quality documentation (40). To overcome these challenges, it is crucial to streamline collaborations between these sectors. Cross-sector training, capacity building, and infrastructure development can enhance the efficiency and effectiveness of the veterinary laboratory sector (41).

It is important to build capacity in VDLs to facilitate effective One Health collaborations and manage zoonotic disease threats (42). The World Organization for Animal Health (WOAH) has played a crucial role in building capacity and strengthening national veterinary services to respond to animal health risks, including those with public health implications (41). Obtaining reliable and valid data from VDLs can support human health, animal health, and One Health initiatives (41). To develop laboratory networks and comply with international health regulations, it is essential to integrate human and animal health laboratories (43). Establishing quality-assured laboratories in resource-constrained settings, together with capacity-building initiatives, can strengthen health systems and promote health equity. Although some health-sector collaborators have focused on providing high-quality laboratory services for tuberculosis, HIV, and cancer care, others have focused solely on either laboratory access or quality rather than addressing both, thereby limiting their overall impact (44).

3.2 International organizations and global health governance

Global organizations, such as the World Health Organization (WHO), Food and Agriculture Organization (FAO), and World Organization for Animal Health (WOAH), support the One Health concept, which focuses on human, animal, and environmental health and sustainability through collaborative approaches (45). These organizations are responsible for improving national veterinary and public health systems and harmonizing global practices to manage zoonotic health risks. In particular, the WOAH performs these tasks by providing standardized frameworks for veterinary health and closely collaborating with the WHO and FAO to coordinate multinational responses (41). Despite its importance for global public health, implementing One Health initiatives requires greater coordination and collaboration among stakeholders. Implementation of these initiatives can be challenging because many initiatives depend on international grants and support. However, the One Health approach varies considerably across World Bank-defined territories (46). Likewise, an assessment of One Health governance in Mexico identified barriers to implementation, including fragmented legal frameworks, limited intersectoral information exchange, and the lack of a dedicated One Health governing body, emphasizing the need for harmonized policies, coordinated surveillance systems, and sustained institutional support (47). Ultimately, strengthening veterinary governance and enhancing stakeholder engagement can help reduce zoonotic disease risks and support novel strategies for mitigating environmental challenges (48). Evidence from the Mekong Region also demonstrates the need to invest in both animal and human health, while strengthening regulatory capacity and local ownership of initiatives to support a coordinated responses to zoonotic disease outbreaks (49).

Global efforts to develop One Health standards for zoonotic disease surveillance have expanded considerably. International alliances and networks, such as the Centers for Disease Control and Prevention’s (CDC) Global Disease Detection platform and the U.S. Department of Defense’s Global Emerging Infections Surveillance (GEIS) program, have collaborated to implement One Health surveillance frameworks (50). Recent advances in molecular techniques and biosensors have improved the detection of emerging pathogens, enabling more rapid and efficient diagnostic procedures (51). However, there are many barriers to standardizing procedures and integrating diagnostics into prevailing national and regional surveillance systems. Therefore, upgrading laboratory capabilities for zoonotic disease surveillance, promoting data-sharing frameworks, and expanding laboratory capacities are critical for improving the prevention and control of zoonotic diseases. Globally, the development of One Health standards to monitor zoonotic diseases is already underway (52). Simultaneously, strengthening system resilience and data-sharing frameworks remains a priority.

One Health approaches rely on cross-sector collaboration among the human, animal, and environmental health sectors to address complex global health challenges, as shown in Figure 1 (52). Research findings and data analyses suggest that implementing One Health collaborations can be both effective and beneficial. For example, One Health approaches have been successfully applied to the prevention and control of Hendra disease in Australia and rabies control in Africa, demonstrating the value of multisectoral partnerships (53). Positive outcomes have also been reported despite bureaucratic and organizational challenges to implementation. Many worldwide forums and supranational organizations aid such partnerships. The tripartite arrangement among the FAO, WOAH, and the WHO is one such example (48). Yasobant et al. (54) also identified several forms of collaboration, including solution-based, level-based, and third-party-based approaches. Although data are available for many global One Health initiatives, documented experience with solution-based cooperation during emergencies remains limited in India. Evidence of level-based initiatives has also been reported in the same study. Conrad et al. (55) suggested strengthening learning networks and cross-disciplinary research to facilitate the broader implementation of One Health approaches for addressing complex health challenges.

3.3 Capacity building in low-resource settings

The system of One Health diagnostics and near-patient testing is hindered in low- and middle-income countries (LMICs) by several critical challenges. These issues include ineffective leadership, inadequate resources, infrastructure deficits, variable demand throughout the supply chain, political instability, and limited access to modern diagnostic technologies. To address these challenges, LMICs are recommended to incorporate a phase-gate model for product development and execution. Yopa et al. (56) recommended establishing a streamlined supply chain management system to improve access to point-of-care (POC) diagnostics. This approach would ensure the efficient delivery of products to the facilities and homes where they will be used. The implementation of One Health diagnostics and point-of-care testing can be facilitated through core enablers, such as the development of reference frameworks for product choice and execution. Other enablers include cross-sector collaboration and the provision of essential resources, with a focus on health systems to improve the management of many emerging global health issues in the future (56).

In low-resource settings, strengthening One Health diagnostic capacity requires investments across the entire diagnostic pathway, from pathogen detection and surveillance to laboratory confirmation and reporting. Molecular diagnostics have gained prominence in low-resource settings because of their speed, portability, flexibility, and ability to improve outbreak detection and environmental surveillance, as evidenced by the advancement of SARS-CoV-2 testing capacity in Nigeria (57). Point-of-care testing could offer even greater accessibility for improving access to early diagnosis in remote and low-resource communities, but depends heavily on the presence of quality management systems, trained operators, quality assurance programs, reliable specimen transport mechanisms, and appropriate referral mechanisms for confirmatory testing. Capacity-building initiatives must also reinforce veterinary and public health laboratory networks, which are essential for disease surveillance, zoonotic pathogen detection, and global health security. Sustained investments in laboratory expertise, workforce training, and infrastructure, including through programs such as the WOAH Laboratory Twinning Program, have led to improvements in diagnostic preparedness and strengthened the capacity for integration into One Health surveillance systems (58). Quality management systems, proficiency in pathogen identification, and capacity for antimicrobial susceptibility testing have also been reinforced through external quality assessment programs, with hybrid virtual and on-site approaches providing pragmatic opportunities to strengthen laboratory systems across One Health reference laboratories in resource-constrained settings (59).

Capacity building within health and One Health systems is essential for addressing animal and public health risks. This need is particularly evident in low- and middle-income countries (60). Postgraduate training programs, professional associations, and hands-on research are some featured measures. Consequently, researchers have identified improvements in individual competencies, broader practical knowledge, and regional partnerships (61). These partnerships are particularly important because they facilitate responses to regionally endemic diseases while addressing their social, cultural, and economic dimensions (62). Ensuring a sustainable workforce requires addressing challenges, including conflicting priorities, sector-focused funding, and a lack of certainty across sectors (60).

The development of the workforce is a key component of strengthening One Health diagnostic systems, particularly in low- and middle-income countries, where shortages of skilled personnel and weak laboratory quality systems may delay diagnosis and disease response. Capacity-building efforts should provide laboratory and field staff with skills in biosafety, specimen collection and handling, diagnostic assay optimization, data management and analysis, quality management, and outbreak response. Experiences in Uganda suggest that field placements and experiential learning can substantially improve zoonotic disease management and risk assessment, including actionable intelligence, laboratory investigations, and cross-sector communication for future health professionals (63). Similarly, laboratory accreditation and quality management systems have provided organized frameworks for enhancing diagnostic accuracy, minimizing testing errors, and strengthening laboratory performance through standardized procedures and proficiency testing (64). Mentorship, external quality assessment, and recurrent, dedicated professional training have also been shown to support sustainability in workforce development, as in Guinea, where post-Ebola laboratory strengthening programs enhanced public health and veterinary laboratory capacity for surveillance, diagnostics, and outbreak response (65). Likewise, targeted workforce development programs in Egypt through multidisciplinary One Health training initiatives have shown that such programs can strengthen disease surveillance, research capacity, and cross-sector collaboration in settings vulnerable to emerging zoonotic diseases (66). Similarly, a regional capacity-strengthening initiative across six East African countries demonstrated that organized training, mentoring, and bioinformatics education can improve genomic surveillance capacity, antimicrobial resistance monitoring, and the sustainable integration of genomics into national public health surveillance systems (67).

The implementation of sustainable One Health approaches in low- and middle-income countries aims to improve the health status of the population by addressing the interconnections among animals, humans, and the environment (68). For example, global networking, education, and community mobilization have all been identified as essential. Programs designed to prevent and control emerging zoonotic diseases, including animal vaccination campaigns, are more effective when they are accessible to all communities rather than only to those able to afford them (69). However, many regions still lack the essential framework, and pathogen detection at the source in the regional population is crucial (29). According to Gebreyes et al. (70), several strategies can improve resource utilization and sustainability, both of which are critical for capacity building. These strategies include developing science-based management guidelines, establishing certified diagnostic laboratories, mentoring skilled personnel, and sharing essential information through a centralized data hub.

Both coordinated investment in laboratory infrastructure, governance, and financing, and the integration of surveillance systems are needed for sustainable capacity building to advance One Health diagnostics. Key priorities include establishing accredited veterinary and public health laboratories, shared data platforms, and interoperable reporting systems that enable public health and veterinary surveillance to better detect and respond to zoonotic disease threats (70). The implementation of effective surveillance systems also requires robust diagnostic governance, standardized data-sharing systems, and multisectoral collaboration to overcome fragmented infrastructure and interoperability barriers (71). Furthermore, One Health capacity-strengthening initiatives should prioritize locally important zoonotic diseases, antimicrobial resistance, and emerging infectious threats while linking laboratory networks with surveillance and reporting systems (72). By integrating laboratory strengthening with surveillance priorities, low-resource settings can improve early outbreak detection, reduce diagnostic inequities, and enhance preparedness for public health emergencies before widespread transmission occurs. Collectively, these measures can strengthen health systems, reduce vulnerability, and contribute substantially to global health security (69).

4 Technological innovations in One Health diagnostics

4.1 Emerging technologies in diagnostics

To aid One Health diagnostics and zoonotic disease data management, many technologies and frameworks are available. In both high- and low-income countries, e-health technologies, often combined with networked tracking devices and biomedical sensors, are widely used for real-time data collection, as shown in Figure 2. Big data analytics, artificial intelligence techniques, and wireless sensing can support real-time monitoring, early detection of anomalies, and outbreak forecasting (73). In the United States, the National Notifiable Diseases Surveillance System and the National Animal Health Monitoring System, along with other collaborative surveillance networks, support interdisciplinary data sharing and collaborative analysis. Geographic information systems and molecular epidemiology facilitate the visualization and analysis of human, animal, and environmental health data, together with zoonotic disease transmission routes (74).

An emerging framework for One Digital Health (ODH) embeds One Health values into digital technologies to enable data-driven surveillance, disease monitoring, and decision-making, as shown in Figure 3 (75, 76). At the conceptual level, ODH has been implemented through integrated digital disease surveillance systems, such as the Electronic Integrated Disease Surveillance System (EIDSS), which integrates data from epidemiological, laboratory, veterinary, and environmental sources to facilitate disease monitoring and control (26). More recently, One Health surveillance initiatives have adopted interoperable data platforms, wastewater-based epidemiology, genomic surveillance, and other advanced analytical tools to monitor pathogens, antimicrobial resistance, and environmental health indicators across sectors (77). Furthermore, additional solutions (artificial intelligence, big data analytics, and federated data-sharing frameworks) across cross-sector actors have been proposed to improve interoperability, enable real-time risk assessment, improve outbreak prediction, and facilitate equitable sharing of health data (78). Modeling approaches based on geographic information systems (GIS), risk assessment, and artificial intelligence have also demonstrated significant potential for disease surveillance, outbreak prediction, and evidence-based decision-making within One Health frameworks, particularly for emerging infectious diseases such as COVID-19 (79). Overall, these advances demonstrate that ODH is not just theoretical; it provides practical pathways for integrated surveillance, diagnostic preparedness, and coordinated public health action.

Next-generation sequencing (NGS) has revolutionized pathogen and molecular diagnostics, offering innovative and cost-effective genome sequencing with high-throughput capabilities. Current NGS applications for diagnostic microbiology include pathogen detection, rapid bacterial typing, and outbreak management (80). This has facilitated the development of a global digital pathogen surveillance system that integrates advanced disease detection frameworks with genomic diagnostics (81). However, challenges remain, mainly related to data analytics and implementation in routine practices. Metagenomic NGS has emerged as a highly accurate tool for identifying pathogens and other microorganisms, particularly in clinical cases for which conventional diagnostic methods are insufficient. Despite these limitations, the One Health framework has identified numerous opportunities for NGS implementation in One Health diagnostics owing to the extensive genomic overview of zoonotic pathogens (81). Furthermore, to enable and support the successful integration of NGS into One Health surveillance, harmonized bioinformatics pipelines, benchmark validation strategies, and quality assurance frameworks are needed to ensure reliable identification of AMR determinants across the human, animal, food, and environmental sectors (82).

The use of artificial intelligence and machine learning in healthcare has transformed diagnostic practice and disease management, including the management of zoonotic diseases. For example, AI-driven models can predict disease outbreaks and transmission patterns, enabling public health agencies to respond more rapidly (83). Recent evidence suggests that machine learning has considerable potential for clinical diagnostics, epidemic forecasting, and intervention optimization; however, major gaps persist in its application to the animal–human interface, spillover prevention, and surveillance in low-resource settings (84). Artificial intelligence has shown potential in combating antimicrobial resistance through rapid pathogen identification, AI-enabled surveillance and early warning systems, integration of cross-sector data, and support for antibiotic stewardship and drug discovery. However, challenges related to data standardization, model transparency, and implementation exist (85). Machine learning improves the timeliness and accuracy of rapid diagnostics and risk evaluation. In genomic medicine, AI supports drug discovery and repurposing, data analysis strategies, and precision medicine decision-making. In general, AI depends on human expertise and has the potential to make healthcare more patient-centered. Rather than replacing healthcare professionals, AI serves as a complementary tool that supports clinical decision-making and expands professional capabilities. Concurrently, specific ethical considerations may challenge the development of AI in healthcare, such as patient autonomy over data use, privacy concerns, and potential algorithmic biases. Nonetheless, prevailing ethical recommendations may facilitate this collaboration and provide a pathway toward a future in which human expertise and technological innovation coexist to optimize patient outcomes (86).

4.2 Data sharing and interoperability

For One Health supervision and diagnostics, data sharing is crucial. However, a range of other challenges must be addressed. These include stewardship, privacy, and regulatory alignment issues. The demand for greater integration of available data is a common problem that hinders effective decision-making in public health (87). Moreover, integrated surveillance is further limited by sectoral fragmentation, inconsistent terminology, and segregated datasets (88). In a systematic review of integrated human and animal health surveillance systems, interoperability and semantic consistency were identified as the primary mechanisms for data integration and were shown to improve surveillance sensitivity, timeliness, and data quality (89). Administrative, legal, and structural barriers across ministries and sectors further limit data integration and coordinated surveillance. An effective approach is the One Digital Health approach, which encompasses a set of solutions, such as data standardization, synchronization, automation, strategic planning, and governance (87). The development of a strong business case for One Health surveillance, which comprises economic tactics and synchronized training, is another solution (90). These approaches can help overcome the identified challenges, improve the management of infectious diseases, ensure timely public health responses, and support the development of medical countermeasures (91).

Many hurdles impede collaboration in One Health systems, including fragmented data systems that require harmonization, the lack of standardized organizational frameworks, and limited interdisciplinary integration. These challenges hinder effective surveillance and diagnostic activities in public health. However, innovative approaches are needed to improve data collection, strengthen research infrastructure, and ensure sustainable financial support. Research on developing approaches for accurate metadata integration and strategies, involving environmental deoxyribonucleic acid (DNA) monitoring and computational procedures, can encourage cross-sector integration among previously separated research fields (92). Moreover, to achieve significant collaboration among stakeholders from different fields, such as the food sector, environmental experts, veterinary medicine, and other related fields, practitioners must consider coordinated and integrated efforts. Despite these advances, the understanding of how to monitor and evaluate One Health implementation remains limited. Such surveillance and analytical methodologies should be enhanced to demonstrate the added value of One Health approaches within health systems (dos S. 93). Evidence from Cameroon also underscores the need for standardized reporting and information-sharing mechanisms to support effective One Health surveillance. A systematic review and meta-analysis by Mouiche et al. (94) reported high levels of multidrug resistance across the human, animal, and environmental sectors and identified substantial gaps in standardization, reporting, and the use of surveillance data for AMR diagnostics. The authors highlighted the importance of ongoing data sharing, large-scale collaboration, and stakeholder coordination to enhance integrated AMR surveillance systems and support evidence-based interventions.

Case studies demonstrate that the diagnostic value of One Health data systems depends not only on data collection but also on the ability to integrate laboratory results, pathogen metadata, genomic information, antimicrobial resistance profiles, and epidemiological context across sectors. Various case studies have documented the collection of One Health data. For example, it is feasible to collect genomic data using Gene Ontology annotations or proteomic data using the Cancer Biomedical Informatics Grid project. Case studies have shown that collaboration and integrated multisectoral approaches can help overcome bureaucratic hurdles and improve One Health outcomes (53). There is also a case of specialized data collection infrastructure for zoonotic diseases supported by the World Health Organization (95). Finally, a case of local collaboration between animal and human health repositories in Denmark has demonstrated success in terms of disease surveillance and management (96). These case studies demonstrate that ontologies, global benchmarks, tools, and frameworks can facilitate interdisciplinary and cross-sector data integration. However, further efforts are required to address legal issues related to regulatory restrictions and data sharing and to enhance database connections for standardized data use within One Health systems.

4.3 Diagnostic preparedness for emerging pathogens

There are many hurdles and difficulties in managing diagnostic preparedness for emerging zoonotic pathogens, including restricted access to samples and reagents, inconsistent funding, insufficient governmental and community-level testing capacity, and the need to incentivize companies to develop diagnostics. Addressing these challenges requires collaboration among multiple stakeholders, strengthened diagnostic capacity across health systems, and sustained financial investment. These challenges are further exacerbated by limited capacity in vulnerable regions to detect and respond to potential outbreaks at the community level. Related activities include hazard identification and the continuous revision of preparedness plans to address emerging risks. Diagnostic and monitoring tools should also be accessible in reference laboratories (97). A systematic review of zoonotic tuberculosis surveillance identified substantial gaps in surveillance coverage and One Health integration worldwide, with the majority of countries relying on passive surveillance systems despite ongoing risks of Mycobacterium bovis transmission (98). Understanding the complex social dynamics of disease transmission, along with relevant environmental factors, is essential and should be incorporated into research and preparedness efforts. Furthermore, alongside the human–animal health nexus, community outreach and surveillance are essential and should be supported. Additional priorities include enhancing governmental and global research agendas by utilizing needs assessments to identify the most important priorities for funding organizations (29).

Improving diagnostic preparedness for infectious disease outbreaks requires investment in assay development, laboratory readiness, field-deployable testing systems, quality assurance, genomic surveillance, automated reporting, and cross-sector specimen referral mechanisms. Public–private collaboration is crucial for rapid diagnostic advancements during non-outbreak periods, which represent a critical phase of preparedness. This includes addressing internal and external challenges, including inadequate financial incentives and dispersed financial support. Integrating diagnostics with the prevailing surveillance system for predictive detection and responses is crucial (99). Moreover, disease reporting systems should be strengthened through automated digital laboratory-based reporting, closer integration and clinical data exchange, and greater use of laboratory diagnostic procedures. Moreover, integrated measures should be employed to strengthen the health service system capability with adequately enhanced and regulated budgetary resources and collaboration with existing systems. Many other measures should be employed to improve preparedness efforts, including expanding international stakeholder collaboration and broadening existing vaccination initiatives to support diagnostic evaluation and production support (100).

International collaboration in diagnostic preparedness also faces several barriers. There is a need for greater financial support, improved access to agents and reactants, enhanced testing capacity, and stronger incentives to increase diagnostic capacity. A holistic approach that connects several stakeholders is required to mitigate the aforementioned barriers. Professionals recommend prioritizing a well-developed health service system, diagnostic capacity, sustained financial support, and diagnostic collaboration with other accessible procedures to achieve these objectives (100). Improving access to diagnostics also requires stronger regulatory collaboration and synchronization, although uncertainty remains regarding future progress (101). To develop effective strategies and strengthen existing systems, international platforms for collaboration should be expanded, and existing initiatives, such as CEPI and Gavi, should be updated to support diagnostic preparedness. Diagnostics should be promoted as an international tool that all people should have access to, and investment should be increased to support their development and deployment (102).

5 Case studies and practical applications

5.1 Zoonotic disease surveillance in the United States

Approximately 75% of emerging infectious diseases are zoonotic and, therefore, present significant public health threats (103). An efficient surveillance system is essential for the early detection and prevention of diseases. In the United States, the National Notifiable Diseases Surveillance System and the National Animal Health Monitoring System have shared valuable data and insights. Both systems work with human and animal health data, demonstrating a One Health approach for disease surveillance. Notably, wildlife surveillance plays a critical role, as many emerging zoonotic diseases are first detected in animal populations. Nevertheless, these organizations are not error-free and face several challenges, including limited sampling capacity and barriers to data sharing. One important strategy for system improvement is the integration of enhanced sampling procedures with data sharing and exchange. This approach is necessary to achieve integrated collaboration, which, in turn, can widen the scope of surveillance. Moreover, it has the potential to foster the development and implementation of the system, facilitating the integration of human, animal, and environmental health in a more unified, extensive, and comprehensive manner (74). Therefore, implementing the approach mentioned above to enhance surveillance systems can improve the capacity to prevent, forecast, and mitigate zoonotic diseases (52).

From a diagnostic perspective, zoonotic disease surveillance in the United States demonstrates the benefits of integrating human, animal, wildlife, food safety, and environmental data into a One Health approach. Early detection and coordinated responses to emerging zoonotic threats are supported by cross-sector surveillance systems, such as national human and animal health monitoring platforms, together with molecular epidemiology, GIS-based mapping, and structured data sharing (74). For example, in antimicrobial resistance surveillance, diagnostic integration is important due to differences in infrastructure, testing capacity, quality management, and data interoperability among public health, veterinary, and environmental laboratories, which limit the development of integrated surveillance networks (104). A comprehensive One Health surveillance framework reinforces the importance of interoperability by linking human, animal, and environmental risk factors through reporting systems, thereby strengthening early outbreak detection, identifying signals of unusual transmission patterns, and supporting timely, evidence-based interventions (6).

Several procedures have been implemented in the United States to strengthen zoonotic disease surveillance. Some of these include molecular epidemiology and geographic information systems (GIS). For example, GIS supports epidemiological mapping by enabling the visualization, communication, and analysis of disease transmission patterns, transmission pathways, risk maps, and protective zones for zoonotic disease prevention (105). Furthermore, these systems can integrate animal health information with social determinants, production, and environmental information to generate evidence-based rapid response strategies for animal health threats, thereby enhancing public health. Thus, the issue of technology integration has been resolved. The One Health approach, which states that population health is related to animal and environmental health, has aided the integration of technologies applied in different systems. Some notable drawbacks have also been identified, such as limited resources, data reliability, data transparency, and complex, evolving, and sometimes costly governmental and regulatory environments (74). In contrast, GIS offers numerous opportunities for epidemiological applications and can help address challenges related to geographic factors, making it an important tool for improving public health outcomes.

The development of One Health surveillance plans for zoonotic diseases has generated significant insights, and the implementation of modern technologies for interpreting and illustrating data has been a key accomplishment in this field. Nevertheless, significant challenges remain, including leadership gaps, inadequate adherence to the Findable, Accessible, Interoperable, and Reusable (FAIR) data guidelines, and insufficient evaluation from a One Health perspective (74). The main challenges in the humanitarian setup include incoherent policies, lack of financial support, and fragile governance operations that affect the implementation of One Health strategies (106). The issues mentioned above and their impact on the effective execution of the selected strategy need to be mitigated. Additionally, it will be essential to establish regulatory agencies, continuously strive to demonstrate effectiveness, and enhance cross-sector integration (107).

5.2 International case studies on One Health interventions

International case studies are presented through a particular diagnostic lens. The focus is not just on One Health collaboration but also on the role that diagnostic systems play as enablers or barriers to pathogen detection, laboratory confirmation, source tracing, genomic surveillance, environmental monitoring, and outbreak response (Figure 4). The selected case studies link human, domestic animal, wildlife, food chain, or environmental data with diagnostic or surveillance actions.

An example of veterinary–public health diagnostic coordination under the One Health framework is provided in the context of Hendra virus surveillance in Australia. The Hendra virus is a zoonotic henipavirus that persists in flying foxes and has led to rare spillovers to humans and horses, primarily in northeastern Australia (108). Surveillance relies on detecting infection in reservoir hosts and associated horses through integrated wildlife ecology, veterinary outbreak investigation, and the potential reduction of human health risks. For the spillover risk assessment, viral excretion could be detected in pooled urine samples using quantitative RT-PCR, supporting its application in environmental and wildlife-based surveillance through long-term longitudinal monitoring of Australian fruit bats (109). Molecular diagnostics have also enhanced understanding of Hendra virus diversity, including the use of qRT-PCR and next-generation sequencing to identify Hendra virus genotype 2 in flying fox tissues (110). Overall, these studies highlight that collaborative wildlife surveillance, equine diagnostics, molecular typing, and public health response are key to minimizing the risk of zoonotic transmission.

Ebola diagnostic preparedness in sub-Saharan Africa highlights the synergy between rapid field testing and confirmatory laboratory networks. Although RT-PCR is the reference test for confirming Ebola virus disease, outbreaks often occur in remote areas where capacity, trained staff, and laboratory infrastructure are limited. Field-use rapid diagnostic tests can support early triage and outbreak detection. However, since their sensitivity is lower, suspected cases still require RT-PCR confirmation (111). Genomic approaches provide additional power to investigate outbreaks. During the 2014 Ebola outbreak in the Democratic Republic of Congo, metagenomic next-generation sequencing identified the Ebola virus in whole-blood samples. It supported the phylogenetic analysis of outbreak lineages (112). The West African Ebola response further demonstrated that field laboratories, biosafety procedures, validated assays, sample referral systems, and trained personnel are required for effective and sustainable laboratory-based diagnosis, as part of laboratory preparedness (113).

The SARS outbreak of 2002–2003 demonstrated the importance of diagnostic preparedness and coordinated surveillance for emerging zoonotic coronaviruses. Laboratory diagnosis of SARS-CoV infection relied on multiple approaches, including reverse transcription polymerase chain reaction (RT-PCR), serological testing, and virus isolation, although early detection remained challenging because test sensitivity varied across disease stages and diagnostic methods. Epidemiological investigation, rapid laboratory confirmation, and international collaboration were essential for tracing transmission chains and supporting outbreak control. From a One Health perspective, SARS highlighted the need for early pathogen detection, coordinated laboratory reporting, wildlife and animal market-surveillance, rapid data sharing, and stronger communication among the public health, veterinary, and environmental sectors. These lessons remain relevant for preparedness against novel coronaviruses and other emerging zoonotic pathogens (114).

The COVID-19 pandemic exposed the importance of integrated diagnostic systems for pandemic preparedness, surveillance, and outbreak response. Although RT-PCR has high analytical sensitivity and remains the reference standard for SARS-CoV-2 confirmation, its large-scale performance is limited as it requires specialized equipment, trained staff, and adequate laboratory infrastructure. Although molecular diagnostics benefit from speedier point-of-care results within minutes for early case detection and public health decision-making, rapid antigen tests have limited use as stand-alone confirmatory tools owing to their lower sensitivity, particularly among individuals with low viral loads (115). This was further supported by genomic surveillance using whole-genome sequencing (WGS) platforms that can generate near-complete SARS-CoV-2 genomes and detect mutations arising from transmission dynamics and public health monitoring (116). From a One Health perspective, wastewater and environmental surveillance have complemented clinical testing through population-level monitoring of viral circulation and strengthened early warning systems at the human–animal–environment interface (117). Collectively, molecular testing, genomic surveillance, and environmental monitoring demonstrate the critical value of integrated diagnostic networks for pandemic response and preparedness for future zoonotic diseases detected in wildlife.

International case studies demonstrate that collaborative One Health diagnostics rely on integrated multisectoral surveillance systems and the ability to connect human, animal, wildlife, and environmental data. Despite differences in epidemiological contexts, the Hendra virus, Ebola virus disease, and COVID-19 case studies all demonstrate the central role of RT-PCR-based molecular diagnostics in laboratory confirmation and outbreak response (109, 111, 115). Additionally, the case studies highlight the complexity of diagnostic preparedness, which extends beyond the ability to perform tests and encompasses aspects such as field-testing capacity, trained personnel, biosafety standards, sample referral pathways, and coordinated laboratory networks (111, 113, 115). The value of genomics in pathogen characterization, source tracing, and monitoring pathogen evolution was further exemplified as genomic surveillance was introduced as an important genomic measurement through the identification of Hendra virus, diversity of genotypes, phylogenetic investigations of multiple Ebola virus outbreaks, and entire genomic relatedness of SARS-CoV-2 variants (110, 112, 116). Environmental surveillance also contributes valuable information for early warning and risk assessment, such as wildlife-based monitoring of the Hendra virus and wastewater surveillance during the COVID-19 pandemic (109, 117). Overall, these examples collectively highlight the vital role that data sharing, integrated laboratory networks, and equitable access to diagnostic tools play in reinforcing One Health surveillance, monitoring, preparedness, and responses to emerging zoonotic threats (113, 117).

5.3 Operationalizing One Health diagnostic systems in practice

Functional diagnostic pathways that integrate sample collection, laboratory analyses, data sharing, and response actions across the human, animal, food safety, and environmental sectors are essential for effective One Health implementation. Whole-genome sequencing (WGS) has further strengthened these pathways in foodborne disease surveillance, enabling unambiguous and rapid characterization of pathogens from humans, animals, food, and environmental sources for outbreak detection, source attribution, and harmonized cross-sectoral public health interventions (118). Diagnostic utility also depends on uniform laboratory practices, interoperable data systems, and efficient cross-sector communication to facilitate swift outbreak detection and the identification of potential sources of infection. Similarly, functional pathways also depend on event detection, sample transport, laboratory confirmation, reporting, and coordinated activation of response across the human–animal–environment interface (119). Practical application was demonstrated during the COVID-19 pandemic in Nigeria, where veterinary laboratory infrastructure was mobilized to maintain human sample processing, biosurveillance, genomic capacity, and multisectoral outbreak response, thus supporting an integrated approach to strengthen preparedness and surveillance for emerging zoonotic threats (120).

Constructing functional diagnostic pathways is not sufficient without adequate laboratory networks, skilled human resources, and quality systems. In low-resource settings, implementing One Health requires accredited veterinary and public health diagnostic laboratories connected through shared databases and integrated surveillance systems to enable rapid pathogen detection and response (70). Additionally, workforce development is key, requiring personnel trained in advanced laboratory methods, bioinformatics, biosafety procedures, and genomic investigation to maintain the continuity of diagnostic services. Effective laboratory systems likewise require high-quality management strategies, accreditation requirements, and external quality evaluation schemes that ensure the dependability and comparability of diagnostic results across sectors. In addition, sustained diagnostic capacity requires dependable reagent supply chains, functional specimen referral networks, and adequate biosafety infrastructure, especially during outbreaks when the demand for testing spikes (121). Uganda’s One Health implementation also illustrated the integration of veterinary laboratories into national networks for surveillance and specimen referral, while reinforcing multisectoral workforce development and coordinated outbreak response (122). Experience with rapid-response mobile laboratories further illustrates that accessible diagnostic infrastructure can shorten turnaround times and strengthen surveillance activities; however, accreditation, quality management systems, and the integration of animal and environmental testing remain key challenges to operationalizing One Health diagnostics (123).

Diagnostic evidence is valuable only when laboratory findings are integrated into existing surveillance platforms, risk assessment workflows, and policy-related response mechanisms. Support for interoperability, semantic consistency, interconnectivity, and timely data exchange through integrated human and animal health surveillance optimizes the value of diagnostic information. It enhances sensitivity, data quality, and outbreak detection capacity (89). In the context of AMR surveillance, genomic technologies, including whole-genome sequencing, metagenomics, and CRISPR-based diagnostics, support the identification of resistance genes, real-time monitoring of resistance patterns, early warning, and targeted control measures across humans, animals, and the environment. Digital genomic platforms, including shared online databases, further enable diagnostic and sequence data to support cross-sector tracking and policy decisions (124). Similarly, the Ontario Animal Health Network demonstrates how laboratory data, abattoir findings, and clinical observations can be integrated, interpreted, and communicated to veterinary and agricultural stakeholders, converting surveillance evidence into risk communication, research priorities, and disease mitigation actions (125).

6 Synthesis and future directions in One Health diagnostics

6.1 Comparative methodologies in One Health research

The development of techniques used to diagnose zoonotic diseases reflects the transition from classical approaches to modern technologies and highlights the importance of understanding the function of each approach when used in combination. Consequently, quantitative real-time PCR and metagenomic tools can be described as valuable approaches that enhance the capacity of classical techniques, such as cell culture and histopathology, thereby improving diagnostic certainty (126). At the same time, frameworks under the term One Health have emphasized the connection between animal, human, and ecological health, highlighting the need to address other related threats, including diseases (127). For instance, the importance of the One Health approach is illustrated by emerging zoonotic pathogens that require immediate detection at the local community level (29). For the diagnosis of tick-borne diseases, the development of solutions is closely aligned with the One Health approach, which enhances the collaboration between human and veterinary medicine through the integration of complementary methods (128). Hence, these innovations represent an important advance that enhances society’s capacity to address the growing global health challenges posed by the increasing frequency of infectious diseases.

One Health diagnostics research focuses on cross-sector collaboration, data sharing, and the integrated application of technologies. Studies have shown that multisectoral collaboration plays an important role in addressing human–animal–environment health challenges. The main findings emphasize integrated surveillance, risk assessment, and communication across sectors (129). Structural requirements and landmark achievements supporting transdisciplinary cooperation are reflected in high-resolution metadata acquisition and the application of environmental DNA (eDNA) methods to combine fragmented research fields (92). However, executing transdisciplinarity through multilateral collaboration is linked with power dynamics, disciplinary conflicts, and unsustainable communication models (130). The persistent advancement of One Health in the field of science is supported by accounting for political changes and incorporating diverse perspectives (131). Future research should prioritize the development of sustainable research institutions that provide continuous support for funding and the careers of contributing scientists (92).

Over time, the One Health approach, which interconnects human, animal, and environmental health, has changed significantly. Diagnostic methodologies have evolved from traditional laboratory-based techniques to rapid field-based assays, which are important for disease detection and control (132). Existing research indicates that integrated diagnostic methodologies are needed to interpret data from diverse sources. One Health practitioners increasingly combine computational algorithms with environmental DNA analysis for viral genome detection (92). Moreover, the current One Health approach is now well established in terms of diagnosing and treating a wide range of diseases. A significant shift towards the adoption of structural One Health approaches has highlighted the roles of economic geography, social determinants, and historical settings in disease emergence and spillover (133). The transformation demonstrates that important lessons have been learned from past practices, and they may significantly influence future practices.

6.2 Making connections between studies

The reviewed studies demonstrate that the One Health approach plays a significant role in addressing public health threats and zoonotic diseases at the interface of animals, humans, and the environment. The consensus is that One Health is a cross-sectoral approach that requires combined diagnostics. However, there is some disparity in the definitions of the term, which impacts the focus on animal welfare, health, and monetary values (134). Moreover, implementation challenges exist, including limitations in university engagement, insufficient coordination, inadequate training opportunities, and funding constraints (135). Low-resource settings face additional challenges because of limited financial and technical capacity (70). Regarding solutions, the studies suggest stronger policy integration, more resources, and improved communication (135). Many diagnostic and health-related tools are playing a role in improving the efficiency of efforts and the capacity for planning and evaluation at the One Health level. Nevertheless, they should be properly integrated (136).

The development of the One Health approach is encouraging, but it is still associated with numerous challenges, including cross-disciplinary cooperation and the adoption of new methods. For One Health studies, a systems approach is required, which is especially valuable in researching zoonoses that cause numerous infectious diseases (137). A recently developed framework for organizing One Health research guides investigators through every stage of the research process, from team formation and study design to data analysis and interpretation (138). Despite its potential, the One Health approach has limitations and unresolved controversies, including challenges related to training, policy, funding difficulties, and the complexity of multisectoral collaboration (139). Future studies should focus on the development of effective evaluation techniques to demonstrate the value of the One Health approach using credible evidence (dos S. 93).

The One Health (OH) approach, which integrates animal, human, and environmental health, requires interdisciplinary coordination between specialists to address health issues (dos S. 93). The obstacles to implementing the One Health strategy include funding and sustainability challenges, practical implementation challenges, and ambiguous and poorly funded basis for evaluating One Health interventions. The solution is to employ a participatory modeling approach that will permit policymakers and interested parties to model the possible development of different scenarios, and develop the requisite measures as part of policy-related decision-making (140). To facilitate the necessary transformation and eradicate the division into fragmented research fields, new methods are being developed to collect and evaluate high-quality metadata at a large scale (92). Additionally, promoting multidisciplinary, interdisciplinary, and transdisciplinary technologies in healthcare and One Health can significantly improve the quality of the technology implemented in practice. Additional research priorities include establishing a long-term system of tools for financing research and creating personnel training programs for researchers (92).

6.3 Future research and policy directions

Many recent studies have highlighted deficiencies in One Health diagnostics, particularly in low-resource settings. Major challenges include limited intersectoral coordination, inadequate public awareness, insufficient training and infrastructure, and limited research capacity (141). Simultaneously, the need for accredited laboratories with shared results, science-driven risk management policies, competent staff, and more efficient resource deployment has been noted (70). A bibliometric analysis revealed persistent publication silos among environmental science, human medicine, and veterinary medicine, highlighting the need for more interdisciplinary research in the field and additional One Health journals (142). Schurer et al. (143) examined community-based surveillance studies of zoonotic parasites and evaluated databases with varying levels of data availability worldwide. They noted the contribution of the simultaneous examination of all three One Health areas, the insufficient use of modern molecular methods in polymerase chain reaction (PCR), and the significant shortcomings in laboratory methods. A comparative overview of key diagnostic approaches, including histopathology, cell culture, metagenomics, and quantitative real-time PCR, along with their respective strengths, limitations, and integrative potential, is presented in Table 1.

Table 1. Comparative methodologies in One Health research.

One Health plays a significant role in addressing complex health risks and challenges, particularly zoonotic diseases. However, in developing countries such as India, there are hurdles to its implementation, including low institutional capacities, low policy visibility, and other departmental priorities (de Macedo 158). Improved financing mechanisms and health-related policies can build global health security and pandemic preparedness (159). Therefore, it is important to strengthen policy development and financing strategies. One Health needs to be more widely integrated into health-related policies that support poverty reduction, sustainable development, and livestock diseases control in countries where livelihoods depend on smallholder farming. Incorporating One Health into national policies will also strengthen preparedness planning and help mitigate downstream impacts for various policy objectives (160).

Recent evidence from the Middle East underscores the need for improved one-health approaches to combat AMR. A systematic review by Alkheraije (161) reported significant resistance to critically important antibiotics across the human, animal, and environmental sectors, emphasizing the need for coordinated surveillance systems, laboratory-based monitoring, and context-specific interventions. This underscores the critical need to plan for the integration of multisectoral collaboration, public health policy, and surveillance strategies into a One Health approach to combat the growing burden of antimicrobial resistance. Emerging evidence from low- and middle-income countries in the Asia-Pacific region supports stronger implementation of national antimicrobial resistance action plans through systematic monitoring and evaluation frameworks, integrated human–animal–environment surveillance systems, and improved stakeholder engagement within a One Health framework (162). Another systematic review highlighted the importance of balancing sectoral collaboration, performance, and translation of integrated One Health Surveillance systems using standardized and validated methods (163). Evidence from conflict-affected settings highlights the need for institutionalized governance structures, interoperable surveillance tools, and resilient laboratory infrastructure to support sustainable implementation of One Health and long-term investment in workforce capacity for maintaining preparedness during periods of systemic disruption (164).

Future policy development and implementation efforts should be guided by the two internationally accepted One Health frameworks proposed by the Quadripartite organizations (WHO, FAO, UNEP, and WOAH). The One Health Joint Plan of Action (2022–2026) provides a framework for enhancing multisector collaboration, integrated surveillance, workforce development, and preparedness across human, animal, and environmental interfaces (10). Complementing this framework, the Theory of Change for One Health provides a conceptual framework that connects policy and advocacy, organizational development, and knowledge exchange (11), while the One Health Assessment Tool can facilitate the identification of national capacity gaps and inform priorities for system strengthening (12). Furthermore, WHO–FAO–WOAH One Health antimicrobial resistance surveillance frameworks also provide practical guidance for integrating surveillance systems and data-sharing mechanisms across sectors (13). More recently, the Quadripartite Integrated Surveillance Guidelines for Antimicrobial Resistance and Antimicrobial Use have provided practical recommendations for harmonizing surveillance systems, laboratory networks, and data-sharing mechanisms across human, animal, food, plant, and environmental domains (165). Future One Health initiatives should align with global strategic frameworks and disease elimination targets. For example, the WHO Neglected Tropical Disease Roadmap 2021–2030 advocates integrated One Health approaches to eliminate dog-mediated human rabies by 2030 (166). The adoption of these frameworks may therefore facilitate more coordinated, evidence-based, and sustainable implementation of a One Health diagnostic system worldwide.

The synergistic effect of genomic technologies and recent progress in artificial intelligence is revolutionizing health diagnostics and zoonotic disease management. From one perspective, AI-driven predictive intelligence has the potential to provide cost-effective tools for predicting outbreaks at local and global levels, supporting early detection and accelerating drug development (83). Machine learning and artificial intelligence have unlocked several applications while addressing challenges related to data integrity and volume, thereby advancing the recognition of precision medicine and the discovery of new drugs. These technologies will be critical in controlling future pandemics and ecological crises (86). In contrast, effective pathogen detection also depends on surveillance at high-risk human–animal interfaces within local communities (139). Notably, the majority of these places are particularly vulnerable to undetected outbreaks because they lack the infrastructure required for advanced laboratory diagnostics (29). Thus, future technology development studies should focus on better integrating AI and other intelligent agents with genomic technologies, strengthening pathogen detection networks, community surveillance, and global cooperation.

7 Conclusion

This structured narrative review demonstrates that One Health diagnostics should not be viewed as a set of discrete diagnostic tools but rather as a system that links governance, multisectoral action, laboratory capacity, technological innovations, data interoperability, and workforce development. The synthesis proposes a practical One Health diagnostic framework comprising institutional governance/policy synergy for direction; multisectoral collaboration to coordinate activities across the human, animal, and environmental sectors; laboratory networks that form the technical basis for pathogen detection, confirmation, antimicrobial resistance monitoring, and genomic surveillance; emerging technologies that enhance speed, sensitivity, portability, and analytical capacity; interoperable data systems that support early warning and joint assessment; and capacity building to promote equitable and sustainable implementation, especially in low-resource settings.

The reviewed literature also indicates how collective weakness in any one of these components may limit the overall effectiveness of One Health diagnostic systems. Emerging technologies (PCR, next-generation sequencing, metagenomics, artificial intelligence, machine learning, geographic information systems, mobile health platforms, biosensors, and point-of-care testing) provide value only when integrated into functional laboratory networks, supported by trained personnel, quality management systems, reliable sample referral pathways, and coordinated reporting structures. Likewise, without governance mechanisms, harmonized data sharing, and more sustainable financing, collaboration across sectors will not translate into timely public health action. These results reinforce the need for a systems approach to prepare for diagnostics of emerging zoonotic pathogens, rather than continued reliance on isolated technological or institution-specific investments.

This review is consistent with the development of an integrated One Health diagnostic framework comprising six interdependent pillars: governance and policy, multisectoral collaboration, laboratory systems, technological innovation, data sharing and interoperability, and capacity building. Together, these pillars strengthen early detection, surveillance sensitivity, confirmation of outbreaks, monitoring of antimicrobial resistance, food safety and food security surveillance, and preparedness for threats of emerging infectious diseases at the human–animal–environment interface (71). Future research should focus on implementation-oriented case studies, the assessment of integrated diagnostic models in low-resource settings, and strategies tailored to specific scenarios for linking diagnostic evidence to surveillance, risk assessment, and policy. Since this review employed a structured narrative approach within a predefined framework, the following limitations should be considered: potential selection bias, geographic imbalance, and variability in the depth of the cited evidence.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fitd.2026.1859816/full#supplementary-material

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Footnotes

Edited by: Marco Albonico, Federation of European Societies for Tropical Medicine and Global Health, Belgium

Reviewed by: Ahmed Almohammadi, Ministry of Health, Saudi Arabia

Hala Abou El Naja, Independent Researcher, Cairo, Egypt