Inference on dengue epidemics with Bayesian regime switching models

摘要

Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Assessments against various baseline showed that BRS performs better in characterizing dengue transmissions. The dengue regimes estimated by BRS are characterized by their persistent nature. Next, climate analysis showed no short nor long term associations between classified regimes with climate. Lastly, fitting BRS to simulated disease data generated from a mechanistic model, we showed links between disease infectivity and regimes classified using BRS. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected.