Tang, M. (STAT) – Bayesian Modeling and Scalable Inference for Count Time Series in Infectious Disease Surveillance
Hybrid Event
Real-time monitoring of infectious disease outbreaks calls for statistical models that recover interpretable quantities such as the time-varying reproduction number from noisy count data, track posterior uncertainty, and run on time scales compatible with daily updates. Existing methods address these aims through separate model classes. Discretized Hawkes processes, Poisson autoregressions, and distributed lag models each […]