Multivariate hierarchical frameworks for modeling delayed reporting in count data.

ABSTRACT: In many fields and applications, count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may not be immediately available, instead arriving in parts over time. For short-term decision making, the statistical challenge lies in predicting the total count based on any observed partial counts, along with a robust quantification of uncertainty. We discuss previous approaches to modeling delayed reporting and present a multivariate hierarchical framework where the count generating process and delay mechanism are modeled simultaneously in a flexible way. This framework can also be easily adapted to allow for the presence of underreporting in the final observed count. To illustrate our approach and to compare it

SUBMITTER: Stoner O 

PROVIDER: S-EPMC7540263 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature