Online Program

WITHDRAWN - Integer-Valued Functional Data Analysis for Measles Forecasting (306781)

Keywords: Bayesian modeling, disease, MCMC, prediction, public health, time series

Measles presents a unique and imminent challenge: the disease is highly contagious, yet vaccination rates are declining precipitously in many localities. Consequently, the risk of a measles outbreak continues to rise. To improve preparedness, we study historical measles data both pre- and post-vaccine, and design new methodology to forecast measles counts with uncertainty quantification. We propose to model the disease counts as an integer-valued functional time series: measles counts are a function of time-of-year and time-ordered by year. The counts are modeled using a negative-binomial distribution conditional on a real-valued latent process, which accounts for the overdispersion observed in the data. The resulting framework provides enhanced capability to model complex seasonality, which varies dynamically from year-to-year, and offers improved multi-month ahead point forecasts and substantially tighter forecast intervals (with correct coverage) compared to existing forecasting models. The fully Bayesian approach provides well-calibrated and precise uncertainty quantification for epi-relevant features, such as the future value and time of the peak measles count in a given year.