# Multivariate Hierarchical Frameworks for Modelling Delayed Reporting in Count Data

## TL;DR

This paper introduces a multivariate hierarchical framework for modeling delayed reporting in count data, enabling more accurate predictions and uncertainty quantification, especially useful in epidemiology and similar fields.

## Contribution

The paper presents a novel hierarchical model that jointly captures count generation and delay mechanisms, adaptable to under-reporting, improving predictive performance over existing methods.

## Key findings

- The framework effectively models delayed reporting in dengue case data.
- It outperforms some existing models in predictive accuracy.
- The approach is computationally efficient and adaptable.

## 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. 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.