Enhancing Pandemic Preparedness: An AI-Driven Hybrid Model for Accurate Outbreak Prediction

This research proposes a novel AI-based predictive system that incorporates generic epidemiological systems, including SIR and SEIR models, into the approach of using advanced machine learning algorithms in the prediction of pandemic outbreaks and makes them more accurate. The hybrid model overcomes shortcomings of the existing methods by using real-time data in combination with dynamic representation through a combination of complex dynamics to capture the phenomena of transmission and timely interventions in the public health sector. The procedure is based on thorough data preprocessing, model design, and strict validation, with the metrics being better than the traditional models in their performance. Case studies are used to argue that a model can be used to predict outbreak hotspots and evaluate the effects of interventions and provide useful information to allocate resources and design policies. Even though some issues, such as the quality and ethics of the data, can be mentioned, the flexibility and accuracy of the model are an important breakthrough in the field of epidemiology. The current study highlights the transformational powers of AI to enhance global pandemic preparedness and response and represents future innovations in the field of public health.