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AI-enhanced health informatics frameworks for predicting infectious disease outbreak dynamics using climate, mobility, and population immunization data integration
Open accessAbstract
The growing frequency and unpredictability of infectious disease outbreaks underscore the urgent need for intelligent, data-driven frameworks that can anticipate transmission dynamics and guide timely interventions. Traditional epidemiological models, while valuable, often struggle to incorporate real-time environmental, behavioral, and immunological variables, resulting in delayed or incomplete forecasts. Recent advances in Artificial Intelligence (AI) and health informatics have revolutionized outbreak prediction by enabling the integration of heterogeneous datasets ranging from climate variables and population mobility patterns to vaccination coverage and genomic surveillance. These AI-enhanced informatics frameworks leverage machine learning, deep neural networks, and knowledge graph architectures to uncover hidden correlations across spatial-temporal datasets and to model nonlinear relationships that influence pathogen spread. By synthesizing multisource data, such systems can dynamically adjust predictions based on evolving transmission parameters and population immunity thresholds, thereby improving early warning accuracy. For example, climate indicators such as temperature, humidity, and rainfall correlate strongly with vector-borne disease incidence, while mobility data from transport networks and mobile devices reveal real-time contact dynamics that shape epidemic trajectories. When fused with immunization registry data, AI-driven models can forecast vulnerable clusters and optimize resource allocation for vaccination campaigns. This paper presents a comprehensive review and conceptualization of AI-enhanced health informatics frameworks designed for integrated outbreak forecasting. It highlights the architecture, algorithms, and interoperability standards required for secure, scalable, and ethically governed data integration. By bridging climate science, mobility analytics, and immunological data, these frameworks redefine the predictive capacity of public health systems, enabling precision epidemic preparedness and adaptive policymaking.
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Cited by
Sorted by citations
Bellarmin, M., Veerasami, H., & Jayaprakash, N. (2026) · Decoding Infection and Transmission · doi:10.1016/j.dcit.2026.100079 · Cited by 5
Omogiate, P. M. (2023) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2023.10.2.1099 · Cited by 3
Afzal, S., Mukhtar, M. U., Mustehsan, M. A., & Farooq, M. (2026) · Khyber Medical University Journal · doi:10.35845/kmuj.2026.24260
Metadata
- Authors
- Abi, R., & Adetunji, O.
- Affiliations
- University College Hospital, Ibadan (NG)
- Western Illinois University (US)
- Venue
- International Journal of Medicine Sciences
- Publisher
- Comprehensive Publications
- Published
- 2023-01-01
- Volume
- 5
- Issue
- 1
- Pages
- 21-31
- ISSN
- 2664-8881 (ISSN-L)
- 2664-889X
- OpenAlex
- W7101430261
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 2
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- International Journal of Medicine Sciences (publishedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- climate data, human mobility, vaccination coverage, vector-borne diseases, contact dynamics, knowledge graphs, multi-source data fusion, early warning systems, epidemic preparedness, data interoperability
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- Abi, R., & Adetunji, O. (2023). AI-enhanced health informatics frameworks for predicting infectious disease outbreak dynamics using climate, mobility, and population immunization data integration. International Journal of Medicine Sciences. https://doi.org/10.33545/26648881.2023.v5.i1a.69
