| Title |
Hierarchical Bayesian Model for Correcting Reporting Delays in Dengue Counts |
|
Authors |
Demecillo, Mikee T and Tubo, Bernadette F. |
|
Publication date |
2022 |
|
Journal |
The Philippine Statistician |
| Volume |
71 |
| Issue |
2 |
| Pages |
9-24 |
| Publisher |
Philippine Statistical Association INC |
| 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 n_t,d. In contrast to the currently reported
instances, which seem to be declining, the posterior predictive model on the
given 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. |
| Index terms / Keywords |
Latent Gaussian Model, Nowcast, Count Data |
| DOI |
|
Mindanao State
University - Iligan Institute of Technology