A multivariate approach for correcting reporting delays in infectious disease surveillance
Conference
65th ISI World Statistics Congress 2025
Format: CPS Poster - WSC 2025
Keywords: bayesian hierarchical model, inla, nowcasting
Abstract
Frequently, real-time tracking of epidemics is faced with a concerning issue, the reporting delays of cases and deaths. Delays might occur due to logistical problems, laboratory confirmation, and other reasons. Being able to correct the delay is essential to decision-making with the goal of containing an epidemic. In some cases, the epidemic might be associated with more than one disease, Dengue and Chikungunya are common examples of this phenomenon. We propose a multivariate model to correct reporting delays and accommodate the above-mentioned cases. The model is estimated using the Integrated Nested Laplace Approximation method with the aim of providing faster results under a bayesian paradigm. An application for the corrections of reporting delays of Dengue and Chikungunya in the state of Rio de Janeiro during an epidemic in 2019 is provided. The proposed model reduces uncertainty in the estimation of the true number of reported cases when compared to the use of two univariate models in a bivariate setting. A sensitivity analysis for the choice of the prior distributions is also performed.
Figures/Tables
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