Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking.

ABSTRACT: Achieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. "Nowcast" approaches attempt to estimate the complete case counts for a given reporting date, using a time series of case reports that is known to be incomplete due to reporting delays. Modeling the reporting delay distribution is a common feature of nowcast approaches. However, many nowcast approaches ignore a crucial feature of infectious disease transmission-that future cases are intrinsically linked to past reported cases-and are optimized to one or two applications, which may limit generalizability. Here, we present a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothing) capable of producing smooth and accurate nowcasts in multiple disease settings. We test NobBS on dengue in P

SUBMITTER: McGough SF 

PROVIDER: S-EPMC7162546 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature