Hierarchical Bayesian Model for Correcting Reporting Delays in Dengue Counts

Year: 2022       Vol.: 71       No.: 2      

Authors: Mikee T. Demecillo and Bernadette F. Tubo

Abstract:

Real-time surveillance and precise case estimation are necessary for situational awareness in order to spot trends and outbreaks and establish efficient control actions. The comprehension of the mechanisms of a sudden rise or fall in disease cases that change over time is hampered by the reporting delays between disease start and case reporting. This study uses a flexible temporal nowcasting model with a Bayesian inference for latent Gaussian models built in R-INLA to rectify reporting delays for weekly dengue surveillance data in Northern Mindanao from 2009 to 2010. Additionally, it seeks to quantify all the uncertainties involved in replacing the missing value. The statistical issue is to forecast run-off triangle numbers based on actual counts ????????!,#. In contrast to the currently reported instances, which seem to be declining, the posterior predictive model on thegiven temporal dataset recognizes the fact that there are more dengue cases than there were previously (supporting the actual scenario). This implies that even with delayed data, the model was still able to provide a reliable estimate of the true number of instances. This paper offers a model for nowcasting to aid in dengue control and good judgment on the part of interested authorities.

Keywords: Latent Gaussian Model, Nowcast, Count Data

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