Modeling seasonality of influenza with Hidden Markov Models

Surveillance of respiratory disease requires a baseline model for the expected case load. This baseline follows a characteristically seasonal pattern, with frequent annual increases in the traditional “flu season” months of September through April. After baseline modeling, anomaly detection methods are used to determine the public health significance of variable incidence above baseline. Thus improved baseline models should yield improved results for anomaly detection. Typical approaches to modeling seasonal baseline use cyclic regression models (also known as Serfling’s method) or variations on this approach. We propose an alternative approach, based on Hidden Markov models (HMMs). We demonstrate this approach on pneumonia and influenza (P&I) mortality data available from the Centers for Disease Control and Prevention (CDC), and compare the performance of HMMs to Serfling’s method and related models.