| Abstract |
To achieve real-time scenario in a disease outbreak and precise estimation of parameters in a data-driven modeling technique, it is crucial that report of
count outcomes be accurate. Yet, the inaccuracies or delays in the report of the actual count of dengue victims frequently hinder the timely acquisition of
insights into outbreak dynamics.
This paper employs a Bayesian framework on a temporal model, as introduced by Bastos, et al. [1], to investigate the impact of report delays on
weekly dengue data collected from Northern Mindanao, Philippines spanning from 2009 to 2010. The analysis extends beyond immediate nowcasting of
dengue counts to include simulation studies assessing the impact of 2, 4, 6, and 8 weeks of report delays on the performance of the temporal model.
The results reveal that the model accurately captures eventual reported cases of dengue, even in the presence of significant report delays, thereby providing
valuable insights to public health authorities to refine decision-making processes within dengue control efforts, accounting for potential discrepancies
between reported and actual dengue counts. |
Mindanao State
University - Iligan Institute of Technology