A modelling approach for correcting reporting delays in disease surveillance data

Abstract

One difficult for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistic problems, infrastructure difficulties, etc. However, some notification systems report not only when the case happen, but also when the information enter in the notification system. Based on this two dates, we developed a hierarchical Bayesian model that update the total reporting cases by estimating the delayed cases. Inference was done under an fast Bayesian approach through an algorithm based on integrated nested Laplace approximation (INLA). We apply the proposed approach in dengue notification data from Rio de Janeiro, Brazil.