International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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     2026:7/3

International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Quantum Machine Learning Algorithms for Real-Time Epidemic Surveillance and Health Policy Simulation: A Review of Emerging Frameworks and Implementation Challenges

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Volume: 5 | Issue: 3 | Pages: 1100-1108

Subject: Health Economics

Country: United States

DOI: https://doi.org/10.54660/IJMRGE.2024.5.3.1100-1108

License: CC BY 4.0

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Abstract

This review explores the integration of quantum machine learning (QML) algorithms into real-time epidemic surveillance systems and health policy simulation frameworks. As conventional machine learning models face scalability and interpretability challenges with high-dimensional epidemiological data, QML offers a transformative paradigm by leveraging quantum computing principles to enhance pattern recognition, data compression, and forecasting precision. The study examines emerging QML architectures and their application to real-time tracking of infectious diseases, anomaly detection in public health datasets, and dynamic modeling of transmission patterns. Additionally, it assesses the role of QML in simulating the impact of health interventions, policy shifts, and behavioral dynamics on epidemic trajectories. Emphasis is placed on multi-modal data integration from genomics, environmental monitoring, and mobility trends to refine predictive accuracy. Key implementation challenges—such as quantum decoherence, algorithmic instability, regulatory constraints, and limited access to quantum hardware—are critically analyzed. The paper concludes by proposing a roadmap for the adoption of hybrid quantum-classical frameworks and policy-informed computational modeling to strengthen global epidemic preparedness and responsive health governance.

How to Cite This Article

Olasehinde Omolayo, Ajao Ebenezer Taiwo, Tope David Aduloju, Babawale Patrick Okare, Afeez Ajani Afuwape, David Frempong (2024). Quantum Machine Learning Algorithms for Real-Time Epidemic Surveillance and Health Policy Simulation: A Review of Emerging Frameworks and Implementation Challenges . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 5(3), 1100-1108. DOI: https://doi.org/10.54660/IJMRGE.2024.5.3.1100-1108

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Publication Information

Journal: International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE)

Publisher: Anfo Publication House

ISSN: 2582-7138 (Online)

Frequency: Bimonthly

Language: English

Open Access: Yes - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

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