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  3. Volume 13, Number 1, 2026
  4. Integrating Multiple Data Sources and Artificial Intelligence for Early Detection of Epidemiological Problems: A One Health Perspective

Integrating Multiple Data Sources and Artificial Intelligence for Early Detection of Epidemiological Problems: A One Health Perspective

MMC.
2026;
Volume 13, Number 1
: pp. 298–310
https://doi.org/10.23939/mmc2026.01.298
Received: May 11, 2025
Revised: April 05, 2026
Accepted: April 06, 2026

Benzarbane S., Hachoumi N., Eddabbah M., AitZaouiat C. E., Boussaa S., El Adib A. R., Guezzaz A.  Integrating Multiple Data Sources and Artificial Intelligence for Early Detection of Epidemiological Problems: A One Health Perspective.  Mathematical Modeling and Computing. Vol. 13, No. 1, pp. 298–310 (2026)

Authors:
  1. S. Benzarbane
  2. N. Hachoumi
  3. M. Eddabbah
  4. C. E. AitZaouiat
  5. S. Boussaa
  6. A. R. El Adib
  7. A. Guezzaz
1
LaRTID Laboratory, Higher School of Technology, Essaouira, Cadi Ayyad University
2
High Institute of Nursing Professions and Health Techniques, Ministry of Health
3
LaRTID Laboratory, Higher School of Technology, Essaouira, Cadi Ayyad University
4
Polydisciplinary Faculty of Sidi Bennour, Chouaib Doukkali University
5
Higher Institute of Nursing Professions and Health Techniques, Ministry of Health
6
Faculty Mohammed VI of Medicine, Mohammed VI University of Sciences and Health
7
LaRTID Laboratory, Higher School of Technology, Essaouira, Cadi Ayyad University

The growing complexity of global health challenges, including zoonotic diseases, climate change, and urbanization, demands innovative and interdisciplinary approaches to epidemiological surveillance.  The One Health approach, which emphasizes the interconnectedness of human, animal, and environmental health, provides a holistic lens for understanding and addressing the root causes of disease outbreaks and environmental health risks.  AI techniques, such as machine learning and predictive modelling, enhance the ability to process large-scale, heterogeneous datasets, identify patterns and predict emerging threats with greater accuracy.  However, challenges such as data fragmentation, privacy concerns, and the need for interdisciplinary collaboration remain significant barriers.  This paper explores the integration of multiple data sources, such as satellite imagery, electronic medical records (EMR), weather forecasting, sensor data, industrial activity monitoring, and population movement tracking with artificial intelligence (AI) technologies within a One Health framework.  It also proposes a data-driven surveillance system capable of addressing the health challenges of the 21st century.

One Health
GIS
artificial intelligence
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