The role of predictive analytics in enhancing public health surveillance: Proactive and data-driven interventions
Open accessAbstract
Predictive analytics has transformed public health surveillance, shifting it from reactive to proactive care. Leveraging advanced mathematical tools, artificial intelligence (AI), and machine learning (ML) algorithms, healthcare systems now analyze data to detect patterns, predict outbreaks, and implement timely interventions. This study examines the role of predictive analytics in strengthening disease surveillance, prioritizing resources, and building effective early warning systems. Using qualitative assessments of implemented systems in various healthcare organizations, data was synthesized from case studies and technical evaluations. Sources included health records, environmental data, and social determinants of health. Results demonstrated that predictive analytics significantly enhance early disease detection and resource management. Integrated models yielded higher accuracy than conventional forecasting methods, and organizations using these systems showed improved preparedness and response to health risks. Success factors identified include data quality, integration, and organizational readiness. However, challenges persist in data normalization and system interoperability. The findings underscore the potential of predictive analytics to improve public health outcomes through timely and precise actions, emphasizing the need for robust analytical models and seamless data integration. While promising, successful implementation requires addressing technical, organizational, and ethical issues. These insights offer valuable guidance for healthcare institutions aiming to adopt and enhance predictive analytics tools.
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Sustainability in the nonprofit context requires ongoing alignment between financial stewardship and purpose-driven action.
“Sustainability in the nonprofit context therefore requires an ongoing alignment between financial stewardship and purpose-driven action (Adeniji, et al., 2024, Ezeh, et al., 2024, Liadi, 2024, Olamide & Badmus, 2024).”
From Conceptual Model for Financial Sustainability and Strategic Resource Allocation in Nonprofit Organizations (2025) · cites this paper as Ezeh, et al., 2024
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- Authors
- Ezeh, C. J., Anioke, S. C., Oyewole, S., & David, M. G.
- Affiliations
- Birmingham City University (GB)
- University of New Haven (US)
- Corresponding author
- Chinedu James Ezeh
- Venue
- World Journal of Advanced Research and Reviews
- Publisher
- GSC Online Press
- Published
- 2024-12-30
- Volume
- 24
- Issue
- 3
- Pages
- 3059-3077
- ISSN
- 2581-9615 (ISSN-L)
- OpenAlex
- W4406044771
- Cited by
- 9
- References
- 29
- Citations by year
- 2026: 6 · 2025: 3
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- World Journal of Advanced Research and Reviews (publishedVersion, cc-by-nc-sa)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › General Health Professions › Public Health Policies and Education
- Keywords
- predictive analytics, disease surveillance, epidemic forecasting, early warning systems, early disease detection, resource allocation, data integration, interoperability, data quality, organizational readiness, social determinants of health, artificial intelligence in healthcare, machine learning
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