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Multivariate Hierarchical Frameworks for Modelling Delayed Reporting in Count Data
Get PDFIn 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. In this article we discuss
previous approaches to modelling delayed reporting and present a multivariate
hierarchical framework where the count generating process and delay mechanism
are modelled simultaneously. Unlike other approaches, the framework can also be
easily adapted to allow for the presence of under-reporting in the final
observed count. To compare our approach with existing frameworks, one of which
we extend to potentially improve predictive performance, we present a case
study of reported dengue fever cases in Rio de Janeiro. Based on both
within-sample and out-of-sample posterior predictive model checking and
arguments of interpretability, adaptability, and computational efficiency, we
discuss the advantages and disadvantages of each modelling framework.Comment: Biometrics (2019
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