International Journal of Medical and All Body Health Research  |  e-ISSN: 2582-8940  |  Double-Blind Peer Review  |  Open Access  |  CC BY-NC-SA 4.0

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     2026:7/4

International Journal of Medical and All Body Health Research

e-ISSN: 2582-8940 | Open Access

Artificial Intelligence–Driven Epidemiological Surveillance for Early Detection of Emerging Infectious Diseases and National Health Security

Sarafa Olumide Olalere, Joy Ejenavi Uzu-Okoh

Department of Peace and Development Studies, University of Ilorin, Ilorin, Kwara State, Nigeria; Department of Nursing, University of Benin, Benin City, Edo State, Nigeria

Article Information

Volume / Issue6 / 2 (April-June 2025)
Pages155-164
AccessOpen Access

Publication Timeline

  • Received04 April 2025
  • Accepted05 May 2025
  • Published

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Abstract

Public health systems, socioeconomic stability, and national security are all under constant and growing threat from emerging infectious diseases. Particularly among highly mobile and environmentally sensitive populations, traditional epidemiological monitoring techniques, which mostly rely on manual reporting and delayed laboratory confirmation, frequently fail to identify epidemics in their early phases. The potential of artificial intelligence (AI)-driven epidemiological monitoring as a revolutionary strategy for the early identification of newly emerging infectious diseases and the reinforcement of national health security is examined in this paper. This summarizes recent developments in AI applications for epidemiological intelligence, emphasizing how automated risk scoring, pattern recognition, and spatiotemporal modeling might spot aberrant illness trends before they spread widely. The study looks at how AI-driven surveillance helps national health security by facilitating better coordination between public health, border control, and emergency management organizations, as well as proactive readiness and quick response decision-making through Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Despite these benefits, there are issues with data quality, interoperability, algorithmic bias, ethical governance, and data privacy when implementing AI-based surveillance   systems. These restrictions are particularly noticeable in low- and middle-income nations, where technical capacity and digital infrastructure may be limited. All things considered, AI-driven epidemiological monitoring is a significant development in contemporary disease intelligence, providing strategic value for early outbreak identification, pandemic prevention, and national health security resilience in a world growing more interconnected by the day.

Keywords

Artificial IntelligenceEpidemiological SurveillanceEmerging Infectious DiseasesEarly Outbreak DetectionNational Health Security

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How to Cite

Olalere, S. O., & Uzu-Okoh, J. E. (2025). Artificial Intelligence–Driven Epidemiological Surveillance for Early Detection of Emerging Infectious Diseases and National Health Security. International Journal of Medical and All Body Health Research, 6(2), 155-164. https://doi.org/10.54660/IJMBHR.2025.6.2.155-164

Olalere, Sarafa Olumide, and Joy Ejenavi Uzu-Okoh. "Artificial Intelligence–Driven Epidemiological Surveillance for Early Detection of Emerging Infectious Diseases and National Health Security." International Journal of Medical and All Body Health Research, vol. 6, no. 2, 2025, pp. 155-164. https://doi.org/10.54660/IJMBHR.2025.6.2.155-164.

Olalere, S. O. and Uzu-Okoh, J. E. (2025) 'Artificial Intelligence–Driven Epidemiological Surveillance for Early Detection of Emerging Infectious Diseases and National Health Security', International Journal of Medical and All Body Health Research, 6(2), pp. 155-164. doi: 10.54660/IJMBHR.2025.6.2.155-164.

Olalere SO, Uzu-Okoh JE. Artificial Intelligence–Driven Epidemiological Surveillance for Early Detection of Emerging Infectious Diseases and National Health Security. International Journal of Medical and All Body Health Research. 2025;6(2):155-164. doi:10.54660/IJMBHR.2025.6.2.155-164

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