American Journal of Advanced Multidisciplinary Research and Innovation

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Predictive Analytics for Public Health Surveillance and Disease Prevention

Author(s) Dan M. Frangopol
Country United States
Abstract Public health surveillance plays a critical role in detecting disease outbreaks, monitoring population health, identifying health risks, and supporting evidence-based decision-making for disease prevention and healthcare planning. Traditional surveillance systems often depend on retrospective reporting, manual data collection, and fragmented health information systems, resulting in delayed responses to emerging public health threats. Recent advances in Predictive Analytics, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Internet of Things (IoT), Electronic Health Records (EHRs), Geographic Information Systems (GIS), Cloud Computing, Digital Health Technologies, Wearable Devices, and Natural Language Processing (NLP) have transformed public health surveillance by enabling real-time disease monitoring, outbreak prediction, intelligent risk assessment, and precision public health interventions.
This study presents a comprehensive analysis of Predictive Analytics for Public Health Surveillance and Disease Prevention. A qualitative analytical research methodology based on secondary data is employed to investigate AI-driven disease surveillance models, predictive epidemiology, intelligent health information systems, environmental health monitoring, digital disease detection, and multidisciplinary public health applications. The research evaluates how predictive analytics enhances disease prevention, healthcare preparedness, policy formulation, resource allocation, and global health security.
The findings indicate that predictive analytics significantly improves early outbreak detection, epidemic forecasting, chronic disease risk prediction, healthcare resource optimisation, vaccination planning, environmental health assessment, and emergency preparedness. Artificial Intelligence supports intelligent decision-making, Machine Learning improves predictive modelling accuracy, Big Data Analytics enables population-level health analysis, and IoT-based health sensors provide continuous monitoring of health indicators. Geographic Information Systems facilitate spatial disease mapping, while cloud-based health platforms enable integrated surveillance across multiple healthcare institutions.
Despite these opportunities, challenges remain concerning data privacy, cybersecurity, interoperability, data quality, ethical AI implementation, regulatory compliance, workforce readiness, and health equity. Future research should investigate explainable predictive analytics, federated health intelligence, quantum-enhanced epidemiological modelling, AI-assisted global health surveillance, and integrated One Health surveillance frameworks.
The study concludes that predictive analytics provides a transformative framework for modern public health surveillance by enabling proactive disease prevention, intelligent healthcare planning, timely outbreak response, and sustainable improvement in global public health outcomes.
Keywords Predictive Analytics, Public Health Surveillance, Disease Prevention, Artificial Intelligence, Machine Learning, Epidemiology, Big Data Analytics, Digital Health, Health Informatics, Intelligent Healthcare.
Field Engineering
Published In Volume 2, Issue 4, July-August 2020
Published On 2020-07-01
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