Bridging the implementation gap in One Health diagnostics: from point-of-care technologies to integrated diagnostic ecosystems
DOI:
https://doi.org/10.66585/ohmi.2026.2.0029Schlagwörter:
POC testing, Zoonotic diseases, One Health, Disease surveillance, Diagnostic ecosystemsAbstract
Recent advances in molecular diagnostics and portable sequencing technologies have significantly expanded the technical capabilities of decentralized diagnostic platforms. Point-of-care (POC) diagnostics have emerged as essential tools for improving the surveillance and control of zoonotic diseases within the One Health framework. Despite this, translating these innovations into systematic, routine field implementation remains limited. This review argues that the primary barriers are no longer technological performance but rather the complex challenges of large-scale implementation. These challenges include fragmented governance, regulatory heterogeneity, economic constraints, logistical hurdles, workforce capacity, and insufficient integration among human, animal, and environmental health systems. Through a critical analysis of current evidence, this manuscript identifies a gap between technological innovation and operational implementation and advocates a transition from device-centric innovation to integrated diagnostic ecosystems that support coordinated One Health surveillance. Strategic directions are discussed, including adaptable diagnostic platforms, interoperable diagnostic networks, digital integration of surveillance systems, operational decentralization, and unified regulatory pathways. Collectively, these strategies could accelerate the translation of emerging diagnostic technologies into sustainable field applications, maximizing their potential to strengthen early detection, integrated surveillance, and preparedness for current and future zoonotic threats.
Literaturhinweise
1. Horefti E. The importance of the One Health concept in combating zoonoses. Pathogens. 2023;12(8):977. https://doi.org/10.3390/pat
hogens12080977
2. European Food Safety Authority (EFSA), European Centre for Disease Prevention and Control (ECDC). The European Union One Health 2024 zoonoses report. EFSA J. 2025;23(12):9759. https://doi.org/10.2903/j.efsa.2025.9759
3. Ma J, Guo Y, Gao J, Tang H, Xu K, Liu Q, et al. Climate change drives the transmission and spread of vector-borne diseases: An ecological perspective. Biology. 2022;11(11):1628. https://doi.org/10.3390/biology11111628
4. European Centre for Disease Prevention and Control. World Mosquito Day 2025: Europe sets new records for mosquito-borne diseases. 2025. Available from: https://www.ecdc.europa.eu/en/news-events/world-mosquito-day-2025-europe-sets-new-records-mo
squito-borne-diseases. Accessed 2026 Jun 24.
5. Centers for Disease Control and Prevention, U.S. Department of the Interior, U.S. Department of Agriculture. National One Health framework to address zoonotic diseases and advance public health preparedness in the United States, 2025–2029. U.S. Department of Health and Human Services. 2025. https://www.cdc.gov/one-health/media/pdfs/2025/01/354391-A-NOHF-ZOONOSES-508_FINAL.pdf
6. Zhang Q-Y, Zhang Y-Y, Liu J-S, Li X-C, Zhu Z-L, Feng X-Y, et al. Integrating One Health governance in China: Assessing structural implementation and operational entry points. One Health. 2025;21:101209. https://doi.org/10.1016/j.onehlt.2025.101209
7. Molina-Flores B, Vigilato MAN, Rocha F, Cossivi O, Corrales M, Vásquez Niño GA, et al. Assessment of One Health initiatives from a veterinary public health approach in Latin America and the Caribbean. Trop Med Infect Dis. 2025;10(11):315. https://doi.org/10.3390/tropicalmed10110315
8. Zubair A, Mukhtar R, Ahmed HM, Ali M. Emergencies of zoonotic diseases, drivers, and the role of artificial intelligence in tracking the epidemic and pandemics. Decoding Infect Transm. 2024;2:100032. https://doi.org/10.1016/j.dcit.2024.100032
9. Johnrose D, Sharma M, Gohil A, Singh S. Molecular diagnostics and therapeutic approaches in zoonotic infections. Int J Res Innov Appl Sci. 2025;10(6):694–706. https://doi.org/10.51584/ijrias.2025.10060054
10. Kardjadj M. Advances in point-of-care infectious disease diagnostics: Integration of technologies, validation, artificial intelligence, and regulatory oversight. Diagnostics. 2025;15(22):2845. https://doi.org/10.3390/diagnostics15222845
11. Zhang L, Guo W, Lv C. Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases. Sci One Health. 2024;3:100061. https://doi.org/10.1016/j.soh.2023.100061
12. Dong C, Liu Y, Nie J, Zhang X, Yu F, Zhou Y. Artificial intelligence in infectious disease diagnostic technologies. Diagnostics. 2025;15(20):2602. https://doi.org/10.3390/diagnostics15202602
13. Lotfalizadeh N, Santucciu C, Chisu V, Sepahvand H, Rahdar A, Behzadmehr R, et al. Advances in microbial diagnostics: Machine learning and nanotechnology for zoonotic disease control. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2026;18(1):70050. https://doi.org/10.1002/wnan.70050
14. Elbehiry A, Abalkhail A. Metagenomic next-generation sequencing in infectious diseases: Clinical applications, translational challenges, and future directions. Diagnostics. 2025;15(16):1991. https://doi.org/10.3390/diagnostics15161991
15. Yu X, Yuan Y, Zhou J, Chang M, Zeng S, Zhuang S. Sensitivity enhancement of surface plasmon resonance biosensors based on versatile nanostructures: Principle, fabrication, and illustrative applications. Microsyst Nanoeng. 2026;12(1):45. https://doi.org/10
.1038/s41378-025-01118-8
16. Irkham, Ibrahim AU, Nwekwo CW, Pwavodi PC, Zakiyyah SN, Ozsoz M, et al. From nanotechnology to AI: The next generation of CRISPR-based smart biosensors for infectious disease detection. Microchem J. 2025;208:112577. https://doi.org/10.1016/j.microc.2024.112577
17. Randhawa GS, Soltysiak MPM, El Roz H, de Souza CPE, Hill KA, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One. 2020;15(4):0232391. https://doi.org/10.1371/journal.pone.0232391
18. Gao Y, Liu M. Application of machine learning based genome sequence analysis in pathogen identification. Front Microbiol. 2024;15:1474078. https://doi.org/10.3389/fmicb.2024.1474078
19. Plebani M, Nichols JH, Luppa PB, Greene D, Sciacovelli L, Shaw J, et al. Point-of-care testing: State-of-the art and perspectives. Clin Chem Lab Med. 2025;63(1):35–51. https://doi.org/10.1515/cclm-2024-0675
20. Hansen S, Abd El Wahed A. Point-of-care or point-of-need diagnostic tests: Time to change outbreak investigation and pathogen detection. Trop Med Infect Dis. 2020;5(4):151. https://doi.org/10.3390/tropicalmed5040151
21. Nair CB, Manjula J, Subramani PA, Nagendrappa PB, Manoj MN, Malpani S, et al. Differential diagnosis of malaria on Truelab Uno®, a portable, real-time, microPCR device for point-of-care applications. PLoS One. 2016;11(1):0146961. https://doi.org/10.1371/journal.pone.0146961
22. Mallya SP, Mbelele PM, Kisonga RM, Mtunga DD, Banzi HH, Mpiima SS, et al. Diagnostic accuracy of the TrueNat™ MTB plus assay for detecting pulmonary tuberculosis in adults. PLoS One. 2025;20(12):0327936. https://doi.org/10.1371/journal.pone.0327936
23. Ghouneimy A, Mahas A, Marsic T, Aman R, Mahfouz M. CRISPR-based diagnostics: Challenges and potential solutions toward point-of-care applications. ACS Synth Biol. 2023;12(1):1–16. https://doi.org/10.1021/acssynbio.2c00496
24. Shigemori H, Fujita S, Tamiya E, Wakida S-I, Nagai H. Solid-phase collateral cleavage system based on CRISPR/Cas12 and its application toward facile one-pot multiplex double-stranded DNA detection. Bioconjug Chem. 2023;34(10):1754–1765. https://doi.org/10.1021/acs.bioconjchem.3c00294
25. Yang X, Wang Y, Liu Y, Huang J, Wei X, Tan Q, et al. Rapid, ultrasensitive, and highly specific identification of Brucella abortus utilizing multiple cross displacement amplification combined with a gold nanoparticles-based lateral flow biosensor. Front Microbiol. 2022;13:1071928. https://doi.org/10.3389/fmicb.2022.1071928
26. Lee DJ, Kumarasamy N, Resch SC, Sivaramakrishnan GN, Mayer KH, Tripathy S, et al. Rapid, point-of-care diagnosis of tuberculosis with novel Truenat assay: Cost-effectiveness analysis for India's public sector. PLoS One. 2019;14(7):0218890. https://doi.org/10.1
371/journal.pone.0218890
27. Getahun DA, Layland LE, Hoerauf A, Wondale B. Impact of the use of GeneXpert on TB diagnosis and anti-TB treatment outcome at health facilities in Addis Ababa, Ethiopia in the post-millennium development years. PLoS One. 2023;18(8):0289917. https://doi.org/10.1371/journal.pone.0289917
28. Anwar S, Khan S, Azmi I, Islam KU, Ahmad T, Iqbal J. CRISPR-based molecular detection of SARS-CoV-2, its emerging variants, and diverse pathogens. Diagn Microbiol Infect Dis. 2025;113(4):117062. https://doi.org/10.1016/j.diagmicrobio.2025.117062
29. Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER. The next-generation sequencing revolution and its impact on genomics. Cell. 2013;155(1):27–38. https://doi.org/10.1016/j.cell.2013.09.006
30. Li Y, Shang J, Luo J, Zhang F, Meng G, Feng Y, et al. Rapid detection of H5 subtype avian influenza virus using CRISPR Cas13a based-lateral flow dipstick. Front Microbiol. 2023;14:1283210. https://doi.org/10.3389/fmicb.2023.1283210
31. Sun A, Vopařilová P, Liu X, Kou B, Řezníček T, Lednický T, et al. An integrated microfluidic platform for nucleic acid testing. Microsyst Nanoeng. 2024;10:66. https://doi.org/10.1038/s41378-024-00677-6
32. Kardjadj M. Advances in point-of-care infectious disease diagnostics: Integration of technologies, validation, artificial intelligence, and regulatory oversight. Diagnostics. 2025;15(22):2845. https://doi.org/10.3390/diagnostics15222845
33. Holicki CM, Bergmann F, Stoek F, Schulz A, Groschup MH, Ziegler U, et al. Expedited retrieval of high-quality Usutu virus genomes via Nanopore sequencing with and without target enrichment. Front Microbiol. 2022;13:1044316. https://doi.org/10.3389/fmicb.2022.1044316
34. Quick J, Loman NJ, Duraffour S, Simpson JT, Severi E, Cowley L, et al. Real-time, portable genome sequencing for Ebola surveillance. Nature. 2016;530(7589):228–232. https://doi.org/10.1038/nature16996
35. Kafetzopoulou LE, Pullan ST, Lemey P, Suchard MA, Ehichioya DU, Pahlmann M, et al. Metagenomic sequencing at the epicenter of the Nigeria 2018 Lassa fever outbreak. Science. 2019;363(6422):74–77. https://doi.org/10.1126/science.aau9343
36. Tarbuck NN, Franks J, Jones JC, Kandeil A, DeBeauchamp J, Miller L, et al. Retail milk monitoring of influenza A(H5N1) in dairy cattle, United States, 2024–2025. Emerg Infect Dis. 2026;32(2):238–241. https://doi.org/10.3201/eid3202.251332
37. Zarnegar A. Point-of-care devices in healthcare: A public health perspective. In: Daimi K, Alsadoon A, Seabra dos Reis S, editors. Current and future trends in health and medical informatics. Cham: Springer Nature Switzerland. 2023. p. 75–92. https://doi.org/10.10
07/978-3-031-42112-9_4
38. Dhir A, Bhasin D, Bhasin-Chhabra B, Koratala A. Point-of-care ultrasound: A vital tool for anesthesiologists in the perioperative and critical care settings. Cureus. 2024;16(8):66908. https://doi.org/10.7759/cureus.66908
39. Satheesh SS, Mourya GV. Risk evaluation of point-of-care testing (POCT) devices: Insights from a tertiary care hospital. Cureus. 2025;17(3):e80499. https://doi.org/10.7759/cureus.80499
40. Zu Y, Chang H, Cui Z. Molecular point-of-care testing technologies: Current status and challenges. Nexus. 2025;2(2):100059. https://doi.org/10.1016/j.ynexs.2025.100059
41. Hobbs EC, Colling A, Gurung RB, Allen J. The potential of diagnostic point-of-care tests (POCTs) for infectious and zoonotic animal diseases in developing countries: Technical, regulatory and sociocultural considerations. Transbound Emerg Dis. 2021;68(4):1835–1849. https://doi.org/10.1111/tbed.13880
42. Heidt B, Siqueira WF, Eersels K, Diliën H, van Grinsven B, Fujiwara RT, et al. Point of care diagnostics in resource-limited settings: A review of the present and future of PoC in its most needed environment. Biosensors. 2020;10(10):133. https://doi.org/10.3390/bios10100133
43. Kuupiel D, Bawontuo V, Drain PK, Gwala N, Mashamba-Thompson TP. Supply chain management and accessibility to point-of-care testing in resource-limited settings: A systematic scoping review. BMC Health Serv Res. 2019;19(1):519. https://doi.org/10.1186/s12913-019-4351-3
44. Gavina K, Franco LC, Khan H, Lavik J-P, Relich RF. Molecular point-of-care devices for the diagnosis of infectious diseases in resource-limited settings: A review of the current landscape, technical challenges, and clinical impact. J Clin Virol. 2023;169:105613. https://doi.org/10.1016/j.jcv.2023.105613
45. Selter F, Salloch S. Whose health and which health? Two theoretical flaws in the One Health paradigm. Bioethics. 2023;37(7):674–682. https://doi.org/10.1111/bioe.13192
46. Singh BB, Somayaji R, Sharma R, Barkema HW, Singh B. Editorial: Zoonoses - a One Health approach. Front Public Health. 2023;11:1332600. https://doi.org/10.3389/fpubh.2023.1332600
47. Potockova H, Dohnal J, Thome-Kromer B. Regulation of veterinary point-of-care testing in the European Union, the United States of America and Japan. Rev Sci Tech. 2020;39(3):699–709. https://doi.org/10.20506/rst.39.3.3171
48. Cocco P, Ayaz-Shah A, Messenger MP, West RM, Shinkins B. Target product profiles for medical tests: A systematic review of current methods. BMC Med. 2020;18(1):119. https://doi.org/10.1186/s12916-020-01582-1
49. Biamonte MA, Cantey PT, Coulibaly YI, Gass KM, Hamill LC, Hanna C, et al. Onchocerciasis: Target product profiles of in vitro diagnostics to support onchocerciasis elimination mapping and mass drug administration stopping decisions. PLoS Negl Trop Dis. 2022;16(8):0010682. https://doi.org/10.1371/journal.pntd.0010682
50. Kohli M, Korobitsyn A, Ismail N, Zignol M, Kasaeva T, Dewan P, et al. WHO target product profile for TB detection at peripheral settings: 2024 update. PLOS Glob Public Health. 2025;5(6):0004612. https://doi.org/10.1371/journal.pgph.0004612
51. Donadeu M, Fahrion AS, Olliaro PL, Abela-Ridder B. Target product profiles for the diagnosis of Taenia solium taeniasis, neurocysticercosis and porcine cysticercosis. PLoS Negl Trop Dis. 2017;11(9):0005875. https://doi.org/10.1371/journal.pntd.0005875
52. Feddema JJ, Fernald KDS, Keijser BJF, Kieboom J, van de Burgwal LHM. Commercial opportunity or addressing unmet needs—Loop-mediated isothermal amplification (LAMP) as the future of rapid diagnostic testing? Diagnostics. 2024;14(17):1845. https://doi.org/
10.3390/diagnostics14171845
53. LeMieux J. CRISPR comes of age—as a diagnostic: Hailed for its gene editing power, Sherlock Bioscience's COVID-19 diagnostic is the first CRISPR technology to gain FDA approval. Clin OMICs. 2020;7(3):10–12. https://doi.org/10.1089/clinomi.07.03.17
54. GenomeWeb. OraSure Technologies acquires Sherlock Biosciences for up to $25M. 2024. Available from: https://www.genomeweb.com
/business-news/orasure-technologies-acquires-sherlock-biosciences-25m. Accessed 2026 Jun 30.
55. Fritzler MJ, Choi MY, Satoh M, Mahler M. Autoantibody discovery, assay development and adoption: Death valley, the sea of survival and beyond. Front Immunol. 2021;12:679613. https://doi.org/10.3389/fimmu.2021.679613
56. Peeling RW, Boeras DI, Nkengasong J. Re-imagining the future of diagnosis of neglected tropical diseases. Comput Struct Biotechnol J. 2017;15:271–274. https://doi.org/10.1016/j.csbj.2017.02.003
57. Fleming KA, Horton S, Wilson ML, Atun R, DeStigter K, Flanigan J, et al. The Lancet Commission on diagnostics: Transforming access to diagnostics. Lancet. 2021;398(10315):1997–2050. https://doi.org/10.1016/S0140-6736(21)00673-5
58. Bansal A, Howes A, Arih I. P23 Advance market commitment for point of care diagnostic focused on neonatal sepsis: Estimating costs and effectiveness. JAC Antimicrob Resist. 2024;6(2):dlae136.027. https://doi.org/10.1093/jacamr/dlae136.027
59. Howard M. A market for diagnostic devices for extreme point-of-care testing: Are we ASSURED of an ethical outcome? Dev World Bioeth. 2024;24(2):84–96. https://doi.org/10.1111/dewb.12389
60. Onwe FI, Okedo-Alex IN, Akamike IC, Igwe-Okomiso DO. Vertical disease programs and their effect on integrated disease surveillance and response: Perspectives of epidemiologists and surveillance officers in Nigeria. Trop Dis Travel Med Vaccines. 2021;7(1):28. https://doi.org/10.1186/s40794-021-00152-4
61. Oltean HN, Lipton B, Black A, Snekvik K, Haman K, Buswell M, et al. Developing a One Health data integration framework focused on real-time pathogen surveillance and applied genomic epidemiology. One Health Outlook. 2025;7(1):9. https://doi.org/10.1186/s42522-024-00133-5
62. Ghai RR, Wallace RM, Kile JC, Shoemaker TR, Vieira AR, Negron ME, et al. A generalizable One Health framework for the control of zoonotic diseases. Sci Rep. 2022;12(1):8588. https://doi.org/10.1038/s41598-022-12619-1
63. Keshavamurthy R, Dixon S, Pazdernik KT, Charles LE. Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches. One Health. 2022;15:100439. https://doi.org/10.1016/j.onehlt.2022.100439
64. Djordjevic SP, Jarocki VM, Seemann T, Cummins ML, Watt AE, Drigo B, et al. Genomic surveillance for antimicrobial resistance-a One Health perspective. Nat Rev Genet. 2024;25(2):142–157. https://doi.org/10.1038/s41576-023-00649-y
65. Villanueva-Miranda I, Xiao G, Xie Y. Artificial intelligence in early warning systems for infectious disease surveillance: A systematic review. Front Public Health. 2025;13:1609615. https://doi.org/10.3389/fpubh.2025.1609615
66. Haque S, Mengersen K, Barr I, Wang L, Yang W, Vardoulakis S, et al. Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. Environ Res. 2024;249:118568. https://doi.
org/10.1016/j.envres.2024.118568
67. Ardila CM, Yadalam PK, González-Arroyave D. Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: A systematic review of antimicrobial susceptibility tests. PeerJ. 2024;12:e18213. https://doi.org/10.7717/peerj.18213
68. Kim JI, Maguire F, Tsang KK, Gouliouris T, Peacock SJ, McAllister TA, et al. Machine learning for antimicrobial resistance prediction: Current practice, limitations, and clinical perspective. Clin Microbiol Rev. 2022;35(3):00179-21. https://doi.org/10.1128/cmr.00179-21
69. Scaglione G, Mastroianni N, Rizzo A, Palomba E, Carcione D, Rimoldi SG, et al. Integrating artificial intelligence with genome sequencing against antimicrobial resistance: A narrative review. Front Public Health. 2026;14:1757161. https://doi.org/10.3389/fpubh.2026.1757161
70. Akande OW, Carter LL, Abubakar A, Achilla R, Barakat A, Gumede N, et al. Strengthening pathogen genomic surveillance for health emergencies: Insights from the World Health Organization's regional initiatives. Front Public Health. 2023;11:1146730. https://doi.org/10.3389/fpubh.2023.1146730
71. World Health Organization, Food and Agriculture Organization of the United Nations, World Organisation for Animal Health, United Nations Environment Programme. Quadripartite guidance on One Health integrated surveillance of antimicrobial resistance and use. Geneva: World Health Organization. 2026. https://www.who.int/publications/b/79427
72. Abrokwa SK, Ruby LC, Heuvelings CC, Bélard S. Task shifting for point of care ultrasound in primary healthcare in low- and middle-income countries-a systematic review. EClinicalMedicine. 2022;45:101333. https://doi.org/10.1016/j.eclinm.2022.101333
73. Lim J, Koprowski K, Stavins R, Xuan N, Hoang TH, Baek J, et al. Point-of-care multiplex detection of respiratory viruses. ACS Sens. 2024;9(8):4058–4068. https://doi.org/10.1021/acssensors.4c00992
74. Gao WJ, Liao CX, Li LM. Introduction for One Health joint plan of action (2022–2026). Zhonghua Liu Xing Bing Xue Za Zhi. 2023;44(4):657–661. https://doi.org/10.3760/cma.j.cn112338-20221202-01032
75. European Food Safety Authority (EFSA), Berezowski J, de Balogh K, Dórea FC, Rüegg S, Broglia A, et al. Prioritisation of zoonotic diseases for coordinated surveillance systems under the One Health approach for cross-border pathogens that threaten the Union. EFSA J. 2023;21(3):07853. https://doi.org/10.2903/j.efsa.2023.7853
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