Volume 2 Issue 1 | 2025 | View PDF
Paper Id:IJMSM-V2I1P104
doi: 10.71141/30485037/V2I1P104

Artificial Intelligence-Powered Predictive Modeling for Disease Surveillance and Mitigation: Enhancing Public Health Interventions

Chinaza Felicia Nwakobe, Sunday Okafor, Innocent Onyebuchi Ilouno

Citation:
Chinaza Felicia Nwakobe, Sunday Okafor, Innocent Onyebuchi Ilouno, "Artificial Intelligence-Powered Predictive Modeling for Disease Surveillance and Mitigation: Enhancing Public Health Interventions" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 1, pp. 34-42, 2025.

Abstract:
The integration of Artificial Intelligence (AI) into disease surveillance and mitigation has emerged as a transformative force in public health. By leveraging AI-driven predictive models, there is significant potential to enhance early detection, real-time monitoring, and targeted intervention strategies during disease outbreaks. This paper examines the role of AI-powered predictive modeling in advancing disease surveillance systems, emphasizing its capacity to improve public health outcomes. Key challenges, including data accessibility, model interpretability, and ethical implications, are critically analyzed alongside opportunities to refine prediction accuracy, enable dynamic monitoring, and tailor interventions to specific populations. The study underscores the importance of fostering interdisciplinary collaboration among data scientists, epidemiologists, and policymakers to harness the full potential of AI-driven surveillance tools. Such efforts are essential to strengthening global health systems and ensuring effective responses to emerging public health threats.

Keywords: Artificial Intelligence, Predictive Modeling, Disease Surveillance, Mitigation, Machine Learning, Epidemiology, Healthcare

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