Transformative applications of Artificial Intelligence in infectious disease forecasting and public health decision support systems

This research review examines the transformative role of artificial intelligence in infectious disease forecasting and public health decision support systems. Through analysis of current implementations, technological frameworks, and operational outcomes, this study evaluates the impact of AI-driven solutions on public health management. The research reveals significant advances in three key areas: predictive modeling accuracy, real-time surveillance capabilities, and automated decision support systems. Notable findings include the successful integration of machine learning algorithms for outbreak prediction, the effective use of natural language processing in early warning systems, and the development of AI-driven resource allocation models. The study highlights critical factors for successful implementation, including data quality, ethical considerations, and system interoperability. Implementation challenges identified include data standardization issues, privacy concerns, and the need for specialized training. The findings suggest that strategic integration of AI technologies could substantially improve public health response capabilities while enhancing the efficiency of resource allocation during disease outbreaks. This research provides valuable insights for public health organizations seeking to leverage AI technologies in their disease surveillance and response systems.