Abstract
Dengue fever is a major public health concern in tropical regions, where transmission dynamics is influenced by factors such as climate and vector density. Accurate estimation of the case reproduction number, [Formula: see text], is crucial for predicting and controlling outbreaks, but incomplete reporting complicates this task. This study employs a Bayesian framework to estimate both [Formula: see text] and the generation time distribution [Formula: see text] using weekly dengue data from Singapore (2014–2018). By optimizing [Formula: see text] and [Formula: see text] through maximum likelihood estimation, we account for uncertainties and reporting delays. The results refine estimates of [Formula: see text], capturing the temporal heterogeneity of dengue transmission. This method offers robust predictions, helping public health officials implement timely outbreak control measures.
Keywords
Bayesian inversion, stochastic modeling, Time-series analysis