American Journal of Advanced Multidisciplinary Research and Innovation

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Data Science Applications in Public Health and Disease Surveillance

Author(s) Andrew W. Lo
Country United States
Abstract The rapid growth of digital health technologies, electronic health records, mobile health applications, wearable devices, genomic databases, and real-time surveillance systems has transformed the field of Public Health through the integration of Data Science, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Internet of Things (IoT), Cloud Computing, Geographic Information Systems (GIS), Natural Language Processing (NLP), Predictive Analytics, Digital Twin Technology, Explainable Artificial Intelligence (XAI), Electronic Health Records (EHRs), Remote Sensing, and Health Information Systems (HIS). These technologies enable early disease detection, outbreak prediction, epidemic modelling, healthcare resource optimisation, public health surveillance, and evidence-based policymaking.
This study presents a comprehensive analysis of Data Science Applications in Public Health and Disease Surveillance. A qualitative analytical research methodology based on secondary data is employed to examine data science techniques, intelligent disease surveillance systems, epidemiological analytics, healthcare decision support, implementation challenges, and future research opportunities. The study investigates how AI-driven analytics enhance infectious disease prediction, chronic disease monitoring, vaccination planning, health risk assessment, environmental health surveillance, and emergency preparedness.
The findings indicate that data science significantly improves disease forecasting, outbreak detection, healthcare resource allocation, syndromic surveillance, population health management, and precision public health. Machine learning models analyse large-scale health datasets to predict disease transmission patterns and identify high-risk populations, while deep learning algorithms process medical images and epidemiological data for improved diagnosis and surveillance. Natural language processing extracts valuable insights from clinical notes, social media, and public health reports, whereas GIS-based spatial analytics supports geographic disease mapping and hotspot identification.
Despite these opportunities, significant challenges remain regarding data quality, privacy, interoperability, cybersecurity, algorithmic bias, ethical AI governance, infrastructure limitations, and regulatory compliance. Future research should investigate federated health learning, explainable epidemiological models, AI-powered digital public health twins, real-time genomic surveillance, and privacy-preserving analytics for global disease monitoring.
The study concludes that data science provides a transformative foundation for modern public health and disease surveillance by integrating intelligent analytics, digital health technologies, and multidisciplinary scientific research to support resilient healthcare systems, early epidemic response, and sustainable global health outcomes.
Keywords Data Science, Public Health, Disease Surveillance, Artificial Intelligence, Machine Learning, Predictive Analytics, Electronic Health Records, Epidemiology, Health Informatics, Digital Health.
Field Engineering
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-12-31
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