AI-Enhanced Biosurveillance: Frameworks, Practices, and Futures

AI transforms the field of biosurveillance by enabling faster detection, analysis, and response to public health threats. AI-enhanced biosurveillance combines advanced technologies like machine learning, big data analytics, and real-time monitoring systems to track disease outbreaks, predict health risks, and improve decision-making processes. Modern biosurveillance systems experience ethical, technical, and policy challenges associated with their implementation. Further exploration in AI surveillance may strengthen global health security, improve pandemic preparedness, and create more responsive and efficient public health systems. AI-Enhanced Biosurveillance: Frameworks, Practices, and Futures explores how AI improves biosurveillance systems for detecting, monitoring, and responding to public health threats. It examines the frameworks, technologies, ethical considerations, and future possibilities of AI-driven approaches in enhancing global health security and disease prevention. This book covers topics such as data science, microbiomics, and public health, and is a useful resource for engineers, biologists, academicians, researchers, and environmental scientists.