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, Rc(t), is crucial for predicting and controlling outbreaks, but incomplete reporting complicates this task. This study employs a Bayesian framework to estimate both Rc(t) and the generation time distribution w(ζ|b,λ) using weekly dengue data from Singapore (2014–2018). By optimizing Rc(t) and w(ζ|b,λ) through maximum likelihood estimation, we account for uncertainties and reporting delays. The results refine estimates of Rc(t), capturing the temporal heterogeneity of dengue transmission. This method offers robust predictions, helping public health officials implement timely outbreak control measures.
Bayesian estimation of reproduction number and generation time distribution for dengue in Singapore
Petrillo, Giuseppe
;Lippiello, Eugenio
2025
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, Rc(t), is crucial for predicting and controlling outbreaks, but incomplete reporting complicates this task. This study employs a Bayesian framework to estimate both Rc(t) and the generation time distribution w(ζ|b,λ) using weekly dengue data from Singapore (2014–2018). By optimizing Rc(t) and w(ζ|b,λ) through maximum likelihood estimation, we account for uncertainties and reporting delays. The results refine estimates of Rc(t), capturing the temporal heterogeneity of dengue transmission. This method offers robust predictions, helping public health officials implement timely outbreak control measures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
