Adaptive Real-Time Epidemic Tracking Computational Model

Design a computational epidemiological modeling system capable of ingesting multi-source data streams (social media, healthcare reports, population movement data) and generating dynamic predictive models for disease spread. Create a flexible event-driven architecture that can rapidly reconfigure transmission models, support Bayesian inference techniques, and provide probabilistic forecasting with quantified uncertainty ranges. Implement robust data validation and anomaly detection mechanisms.