Abstract

The increasing frequency of global health crises has highlighted critical limitations in traditional epidemiological surveillance systems, particularly in their ability to provide real-time insights, predictive intelligence, and adaptive response mechanisms. This paper proposes a generalized framework for Generative AI–powered epidemiological modeling platforms designed to enable autonomous disease surveillance at scale. By integrating advanced generative models, including large language models and probabilistic simulation techniques, with heterogeneous data sources such as electronic health records, environmental sensors, mobility datasets, and social media streams, the proposed platform enhances early outbreak detection, forecasting accuracy, and decision support capabilities.

The study explores how generative AI can synthesize realistic outbreak scenarios, fill gaps in incomplete datasets, and dynamically adapt models based on evolving epidemiological patterns. It further examines architectural considerations, including data ingestion pipelines, model orchestration layers, real-time analytics engines, and cloud-native deployment strategies. Key contributions include a modular reference architecture, comparative analysis of generative versus traditional compartmental models, and evaluation metrics for model reliability, explainability, and scalability.

The paper also addresses critical challenges related to data privacy, ethical governance, and model bias, proposing mitigation strategies aligned with global health data standards. Through conceptual modeling and system design perspectives, this research demonstrates how generative AI can transform epidemiological platforms into proactive, autonomous systems capable of supporting public health authorities in timely and informed decision-making.