Bayesian dynamic modeling of time series of dengue disease case counts.

ABSTRACT: The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter

SUBMITTER: Martinez-Bello DA 

PROVIDER: S-EPMC5510904 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature