EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Review Article
Feb 11, 2026 9 6

Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning

Vivian Ukamaka Nwokedi 1 * ,
Patra Chisom Ezeamii 2,
Adepeju Kafayat Olowookere 3,
Oyebamiji Hafeezat Omolabake 4
1 Department of Clinical Pharmacy, Faculty of Pharmacy, University of Benin, Benin City, Nigeria2 Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA3 Department of Nanoscience, University of North Carolina at Greensboro, Greensboro, NC, USA4 Department of Medicine and Surgery, College of Health Sciences, University of Ilorin, Llorin, Nigeria* Corresponding Author
Epidemiology and Health Data Insights, 2(2), 2026, ehdi030, https://doi.org/10.63946/ehdi/17898
Publication date: Feb 11, 2026
Full Text (PDF)

ABSTRACT

Infectious disease surveillance has long been vital in public health, but traditional methods often fall short in detecting emerging threats and understanding pathogen evolution. Recent advances in Next-Generation Sequencing (NGS) have revolutionized genomic surveillance, enabling near real-time monitoring of pathogens at the genetic level. This study explores the integration of real-time genomic surveillance with epidemiological models to enhance disease intervention planning. We examine how combining genomic data with models like Susceptible-Infectious-Recovered (SIR) and Susceptible-Exposed-Infectious-Recovered (SEIR) improves outbreak forecasting, facilitates early detection of new variants, and provides actionable insights for targeted interventions. The integration of NGS data allows for more precise transmission network mapping, better-informed resource allocation, and dynamic policy adjustments. However, challenges persist, including technical limitations, data privacy concerns, and equity in global surveillance capacities. The findings suggest that genomic integration enhances epidemic prediction and response but requires robust policy frameworks, equitable data-sharing practices, and continuous capacity-building efforts in low- and middle-income regions. The future of infectious disease control hinges on advancing technologies like artificial intelligence (AI), cloud computing, and machine learning to improve predictive accuracy and support real-time decision-making. This review underscores the potential of genomic surveillance to transform public health strategies and outlines key steps for effective global collaboration.

KEYWORDS

Genomic Surveillance Next-Generation Sequencing Epidemiological Models Outbreak Forecasting Public Health Data Integration Policy Frameworks

CITATION (Vancouver)

Nwokedi VU, Ezeamii PC, Olowookere AK, Omolabake OH. Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning. Epidemiology and Health Data Insights. 2026;2(2):ehdi030. https://doi.org/10.63946/ehdi/17898
APA
Nwokedi, V. U., Ezeamii, P. C., Olowookere, A. K., & Omolabake, O. H. (2026). Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning. Epidemiology and Health Data Insights, 2(2), ehdi030. https://doi.org/10.63946/ehdi/17898
Harvard
Nwokedi, V. U., Ezeamii, P. C., Olowookere, A. K., and Omolabake, O. H. (2026). Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning. Epidemiology and Health Data Insights, 2(2), ehdi030. https://doi.org/10.63946/ehdi/17898
AMA
Nwokedi VU, Ezeamii PC, Olowookere AK, Omolabake OH. Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning. Epidemiology and Health Data Insights. 2026;2(2), ehdi030. https://doi.org/10.63946/ehdi/17898
Chicago
Nwokedi, Vivian Ukamaka, Patra Chisom Ezeamii, Adepeju Kafayat Olowookere, and Oyebamiji Hafeezat Omolabake. "Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning". Epidemiology and Health Data Insights 2026 2 no. 2 (2026): ehdi030. https://doi.org/10.63946/ehdi/17898
MLA
Nwokedi, Vivian Ukamaka et al. "Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning". Epidemiology and Health Data Insights, vol. 2, no. 2, 2026, ehdi030. https://doi.org/10.63946/ehdi/17898

REFERENCES

  1. Mami DM, Romba M, Patrick M. The Surveillance in Infectious Disease Control Role of Genomics. International Journal of Advanced Multidisciplinary Research and Studies. 2025 Aug 13;5(4):1608-15. Available from: https://www.multiresearchjournal.com/arclist/list-2025.5.4/id-4803
  2. Struelens MJ, Ludden C, Werner G, Sintchenko V, Jokelainen P, Ip M. Real-time genomic surveillance for enhanced control of infectious diseases and antimicrobial resistance. Front Sci. 2024 Apr 25;2:1298248. https://doi.org/10.3389/fsci.2024.1298248
  3. Tiwari S, Dhakal T, Kim BJ, Jang GS, Oh Y. Genomics in Epidemiology and Disease Surveillance: An Exploratory Analysis. Life. 2025 Dec 1;15(12):1848. https://doi.org/10.3390/life15121848
  4. Osei Sekyere J. Next‐Generation Sequencing in Infectious‐Disease Diagnostics: Economic, Regulatory, and Clinical Pathways to Adoption. Microbiol Open. 2025 Dec;14(6):e70104. https://doi.org/10.1002/mbo3.70104
  5. Revez J, Espinosa L, Albiger B, Leitmeyer KC, Struelens MJ, ECDC National Microbiology Focal Points and Experts Group. Survey on the use of whole-genome sequencing for infectious diseases surveillance: rapid expansion of European national capacities, 2015–2016. Front Public Health. 2017 Dec 18;5:347. https://doi.org/10.3389/fpubh.2017.00347
  6. Centers for Disease Control and Prevention. Technical explainer: Infectious disease transmission models [Internet]. 2025 Jan 6 [cited 2025 Oct 7]. Available from: https://www.cdc.gov/cfa-modeling-and-forecasting/about/explainer-transmission-models.html
  7. World Bank. An introduction to deterministic infectious disease models [Internet]. Working Paper No. 161374, Vol. 1. 2021 Jul 2 [cited 2025 Oct 7]. Available from: http://documents.worldbank.org/curated/en/888341625223820901
  8. Duault H, Durand B, Canini L. Methods combining genomic and epidemiological data in the reconstruction of transmission trees: a systematic review. Pathogens. 2022 Feb 15;11(2):252. https://doi.org/10.3390/pathogens11020252
  9. Contreras S, Oróstica KY, Daza-Sanchez A, Wagner J, Dönges P, Medina-Ortiz D, et al. Model-based assessment of sampling protocols for infectious disease genomic surveillance. Chaos Solitons Fractals. 2023 Feb 1;167:113093. https://doi.org/10.48550/arXiv.2301.07951
  10. Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, et al. Next-generation sequencing technology: current trends and advancements. Biology. 2023 Jul 13;12(7):997. https://doi.org/10.3390/biology12070997
  11. Wikipedia. Nanopore sequencing [Internet]. [cited 2025 Oct 7]. Available from: https://en.wikipedia.org/wiki/Nanopore_sequencing
  12. Illumina. NGS methods to identify and track infectious disease threats [Internet]. [cited 2025 Oct 7]. Available from: https://www.illumina.com/areas-of-interest/microbiology/public-health-surveillance/genomic-surveillance.html
  13. Jabłońska A, Sadkowski A, Richert-Przygońska M, Styczyński J. Next-generation sequencing for infectious disease diagnostics in pediatric patients with Malignancies or after hematopoietic cell transplantation: A systematic review. J Clin Med. 2025 Sep 12;14(18):6444. https://doi.org/10.3390/jcm14186444
  14. Papamentzelopoulou M, Vrioni G, Pitiriga V. Comparative Evaluation of Sequencing Technologies for Detecting Antimicrobial Resistance in Bloodstream Infections. Antibiotics. 2025 Dec 12;14(12):1257. https://doi.org/10.3390/antibiotics14121257
  15. Cason C, D’Accolti M, Soffritti I, Mazzacane S, Comar M, Caselli E. Next-generation sequencing and PCR technologies in monitoring the hospital microbiome and its drug resistance. Front Microbiol. 2022 Jul 28;13:969863. https://doi.org/10.3389/fmicb.2022.969863
  16. Lai LM, Chen QG, Liu Y, Zhao R, Cao ML, Yuan L. The value of metagenomic next-generation sequencing in the diagnosis of fever of unknown origin. Sci Rep. 2025 Jan 14;15(1):1963. https://doi.org/10.1038/s41598-025-86295-2
  17. Tolles J, Luong T. Modeling epidemics with compartmental models. JAMA. 2020 Jun 23;323(24):2515-6. https://doi.org/10.1001/jama.2020.8420
  18. Wikipedia. Kermack–McKendrick theory [Internet]. [cited 2025 Oct 15]. Available from: https://en.wikipedia.org/wiki/Kermack%E2%80%93McKendrick_theory
  19. Milgroom MG. Epidemiology and sir models. In: Milgroom MG, editor. Biology of Infectious Disease: From Molecules to Ecosystems. Cham: Springer International Publishing; 2023. p. 253-68. https://doi.org/10.1007/978-3-031-38941-2_16
  20. Mirsaeedi F, Sheikhalishahi M, Mohammadi M, Pirayesh A, Ivanov D. Compartmental models in epidemiology: bridging the gap with operations research for enhanced epidemic control. Ann Oper Res. 2025 Oct 20:1-58. https://doi.org/10.1007/s10479-025-06893-1
  21. Kühnert D, Stadler T, Vaughan TG, Drummond AJ. Simultaneous reconstruction of evolutionary history and epidemiological dynamics from viral sequences with the birth–death SIR model. J R Soc Interface. 2014 May 6;11(94):20131106. https://doi.org/10.1098/rsif.2013.1106
  22. Glennon EE, Bruijning M, Lessler J, Miller IF, Rice BL, Thompson RN, et al. Challenges in modeling the emergence of novel pathogens. Epidemics. 2021 Dec 1;37:100516. https://doi.org/10.1016/j.epidem.2021.100516
  23. Hasan GM, Mohammad T, Shamsi A, Sohal SS, Hassan MI. Applications of Genome Sequencing in Infectious Diseases: From Pathogen Identification to Precision Medicine. Pharmaceuticals. 2025 Nov 7;18(11):1687. https://doi.org/10.3390/ph18111687
  24. Carson J, Keeling M, Ribeca P, Didelot X. Incorporating epidemiological data into the genomic analysis of partially sampled infectious disease outbreaks. Mol Biol Evol. 2025 Apr;42(4):msaf083. https://doi.org/10.1093/molbev/msaf083
  25. Hill V, Ruis C, Bajaj S, Pybus OG, Kraemer MU. Progress and challenges in virus genomic epidemiology. Trends Parasitol. 2021 Dec 1;37(12):1038-49. https://doi.org/10.1016/j.pt.2021.08.007
  26. Oltean-Parke H. Enhancing public health surveillance: integrating genomic and epidemiologic data to inform public health action and one health progress [dissertation]. Seattle (WA): University of Washington; 2023. Available from: http://hdl.handle.net/1773/51162
  27. Wikipedia. COVID-19 Genomics UK Consortium [Internet]. [cited 2025 Oct 22]. Available from: https://de.wikipedia.org/wiki/COG-UK
  28. Carter LL, Yu MA, Sacks JA, Barnadas C, Pereyaslov D, Cognat S, et al. Global genomic surveillance strategy for pathogens with pandemic and epidemic potential 2022–2032. Bull World Health Organ. 2022 Apr 1;100(4):239. https://doi.org/10.2471/BLT.22.288220
  29. World Bank. Strengthening genomic surveillance in Africa: Capacity building beyond COVID-19 [Internet]. 2025 Apr 7 [cited 2025 Nov 7]. Available from: https://www.worldbank.org/en/news/feature/2025/04/07/strengthening-genomic-surveillance-in-afe-africa-capacity-building-beyond-covid-19
  30. Shu Y, McCauley J. GISAID: Global initiative on sharing all influenza data–from vision to reality. Euro Surveill. 2017 Mar 30;22(13):30494. https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494
  31. Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018 Dec 1;34(23):4121-3. https://doi.org/10.1093/bioinformatics/bty407
  32. O’Toole Á, Pybus OG, Abram ME, Kelly EJ, Rambaut A. Pango lineage designation and assignment using SARS-CoV-2 spike gene nucleotide sequences. BMC Genomics. 2022 Feb 11;23(1):121. https://doi.org/10.1186/s12864-022-08358-2
  33. Ochola R. The Case for Genomic Surveillance in Africa. Trop Med Infect Dis. 2025 May 8;10(5):129. https://doi.org/10.3390/tropicalmed10050129
  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 Feb 11;530(7589):228-32. https://doi.org/10.1038/nature16996
  35. Du P, Ding N, Li J, Zhang F, Wang Q, Chen Z, et al. Genomic surveillance of COVID-19 cases in Beijing. Nat Commun. 2020 Oct 30;11(1):5503. https://doi.org/10.1038/s41467-020-19345-0
  36. Ling-Hu T, Rios-Guzman E, Lorenzo-Redondo R, Ozer EA, Hultquist JF. Challenges and opportunities for global genomic surveillance strategies in the COVID-19 era. Viruses. 2022 Nov 16;14(11):2532. https://doi.org/10.3390/v14112532
  37. Wang S, Jiang X, Singh S, Marmor R, Bonomi L, Fox D, et al. Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States. Ann N Y Acad Sci. 2017 Jan;1387(1):73-83. https://doi.org/10.1111/nyas.13259
  38. Zeghbib S, Kemenesi G, Jakab F. The importance of equally accessible genomic surveillance in the age of pandemics. Biol Futura. 2023 Jun;74(1):81-9. https://doi.org/10.1007/s42977-023-00164-5
  39. UK Government Department of Business, Energy and Industrial Strategy. A case study of viral genomic data sharing during the COVID-19 pandemic [Internet]. 2022 Nov [cited 2025 Nov 10]. Available from: https://assets.publishing.service.gov.uk/media/63766ddf8fa8f57721e34e30/intelligent-open-science.pdf
  40. Staunton C, Edgcumbe A, Abdulrauf L, Gooden A, Ogendi P, Thaldar D. Cross-border data sharing for research in Africa: An analysis of the data protection and research ethics requirements in 12 jurisdictions. J Law Biosci. 2025 Jan;12(1):lsaf002. https://doi.org/10.21203/rs.3.rs-4217849/v1
  41. Idahor CO, Esomu EJ, Ogbonna N, Momoh Z, Ogbeide OA, Ikhu-Omoregbe O, et al. Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls. Cureus. 2025 Oct 6;17(10):e93929. https://doi.org/10.7759/cureus.93929
  42. Villanueva-Miranda I, Xiao G, Xie Y. Artificial Intelligence in Early Warning Systems for Infectious Disease Surveillance: A Systematic Review. Front Public Health. 2025 Jun 23;13:1609615. https://doi.org/10.3389/fpubh.2025.1609615
  43. da Silva Mendes VI, Milhomens de Ferreira Mendes B, Moura RP, Mota Lourenço I, Ferreira Alves Oliveira M, Lee Ng K, et al. Harnessing artificial intelligence for enhanced public health surveillance: a narrative review. Front Public Health. 2025 Jul 30;13:1601151. https://doi.org/10.3389/fpubh.2025.1601151
  44. Pinto AD, Birdi S, Durant S, Rabet R, Parekh R, Ali S, et al. Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation. JMIR Public Health Surveill. 2025 Oct 24;11:e68952. https://doi.org/10.2196/68952
  45. World Health Organization. WHO releases new principles for ethical human genomic data collection and sharing [Internet]. 2024 Nov 20 [cited 2025 Nov 18]. Available from: https://www.who.int/news/item/20-11-2024-who-releases-new-principles-for-ethical-human-genomic-data-collection-and-sharing
  46. Moodley K, Cengiz N, Domingo A, Nair G, Obasa AE, Lessells RJ, et al. Ethics and governance challenges related to genomic data sharing in southern Africa: the case of SARS-CoV-2. Lancet Glob Health. 2022 Dec 1;10(12):e1855-9. https://doi.org/10.1016/S2214-109X(22)00417-X
  47. Wikipedia. Bermuda Principles [Internet]. [cited 2025 Nov 18]. Available from: https://en.wikipedia.org/wiki/Bermuda_Principles
  48. Wikipedia. Fort Lauderdale Agreement [Internet]. [cited 2025 Nov 18]. Available from: https://en.wikipedia.org/wiki/Fort_Lauderdale_Agreement
  49. Africa CDC. Pathogen genomic surveillance policy framework [Internet]. 2025 [cited 2025 Nov 18]. Available from: https://africacdc.org/download/pathogen-genomic-surveillance-policy-framework/
  50. Horn R, Merchant J. Ethical and social implications of public–private partnerships in the context of genomic/big health data collection. Eur J Hum Genet. 2024 Jun;32(6):736-41. https://doi.org/10.1038/s41431-024-01608-9

LICENSE

Creative Commons License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.