American Journal of Advanced Multidisciplinary Research and Innovation

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Artificial Intelligence Applications in Public Health Surveillance and Disease Prevention

Author(s) Burcin Becerik-Gerber
Country United States
Abstract Public health surveillance is fundamental to preventing disease outbreaks, monitoring population health, and supporting evidence-based healthcare policies. Traditional surveillance systems often rely on delayed reporting, manual data collection, fragmented health information systems, and limited predictive capabilities, reducing their effectiveness in responding to emerging infectious diseases and chronic health conditions. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Internet of Things (IoT), Cloud Computing, Edge Computing, Geographic Information Systems (GIS), Natural Language Processing (NLP), Electronic Health Records (EHRs), Wearable Health Technologies, and Digital Health Platforms have transformed public health surveillance by enabling real-time disease monitoring, outbreak prediction, risk assessment, and intelligent decision support.
This study presents a comprehensive analysis of Artificial Intelligence Applications in Public Health Surveillance and Disease Prevention. A qualitative analytical research methodology based on secondary data is employed to investigate AI-enabled disease surveillance systems, predictive epidemiological modelling, intelligent contact tracing, vaccination planning, environmental health monitoring, and healthcare decision support. The research evaluates how AI technologies improve early disease detection, outbreak response, healthcare resource allocation, policy development, and population health management.
The findings indicate that AI significantly enhances disease forecasting, anomaly detection, epidemic modelling, healthcare resource optimisation, vaccination programme management, and real-time epidemiological surveillance. Machine Learning algorithms improve disease prediction accuracy, while Natural Language Processing analyses clinical reports, scientific literature, and social media data to identify emerging public health threats. IoT-enabled wearable devices and environmental sensors provide continuous health monitoring, whereas cloud computing facilitates large-scale public health analytics. Furthermore, GIS-based spatial intelligence supports hotspot identification, risk mapping, and targeted intervention strategies.
Despite these opportunities, challenges remain concerning data privacy, ethical governance, algorithmic bias, interoperability, cybersecurity, regulatory compliance, data quality, and workforce preparedness. Future research should investigate explainable AI, federated learning for privacy-preserving surveillance, multimodal epidemiological intelligence, quantum-assisted disease modelling, and equitable AI deployment in global public health systems.
The study concludes that Artificial Intelligence provides a transformative framework for intelligent public health surveillance and disease prevention by enabling proactive healthcare interventions, strengthening outbreak preparedness, improving decision-making, and supporting resilient healthcare systems.
Keywords Artificial Intelligence, Public Health Surveillance, Disease Prevention, Machine Learning, Epidemiology, Digital Health, Internet of Things, Predictive Analytics, Electronic Health Records, Population Health.
Field Engineering
Published In Volume 2, Issue 3, May-June 2020
Published On 2020-05-18
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