Modelling reporting delays for disease surveillance data

TL;DR

This paper introduces a Bayesian hierarchical model using INLA to correct reporting delays in disease surveillance data, improving real-time epidemic tracking and decision-making.

Contribution

It presents a flexible, fast Bayesian approach for adjusting reporting delays and quantifying uncertainty in epidemic data analysis.

Findings

Effective correction of reporting delays demonstrated on dengue data

Model provides uncertainty estimates for adjusted data

Implementation is computationally efficient using INLA

Abstract

One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistic problems, infrastructure difficulties and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast, due to the use of the integrated nested Laplace approximation (INLA). The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and Severe Acute Respiratory Illness (SARI) data in Paran\'a state, Brazil.