Abstract
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 autoregressive processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Posterior predictive checks indicate that BRS replicates temporal trends in Dengue transmissions well and nowcast accuracy assessed using a post-hoc classification scheme showed that BRS classification accuracy is robust even under limited data with the AUC-ROC at 0.935. LASSO-based regression and bootstrapping was used to account for plausibly high dimensions of climatic factors affecting Dengue transmissions, which was then estimated using cross-validation to conduct statistical inference on long-run climatic effects on the estimated regimes. BRS estimates epidemic and endemic regimes of dengue in Singapore which are characterized by persistence across time, lasting an average of 20 weeks and 66 weeks respectively, with a low probability of transitioning away from their regimes. Climate analysis with LASSO indicates that long-run climatic effects up to 20 weeks ago do not differentiate epidemic and endemic regimes. Lastly, by fitting BRS to simulated disease data generated from a stochastic Susceptible-Infected-Recovered model, mechanistic links between infectivity and regimes classified using BRS were provided. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected.
Highlights
An estimated 390 million dengue infections occur annually creating considerable health and economic burdens [1]
A mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world
Assessments against various baseline showed that Bayesian regime switching (BRS) performs better in characterizing dengue transmissions
Summary
An estimated 390 million dengue infections occur annually creating considerable health and economic burdens [1]. Widespread ongoing urbanization and greater host movement rates via both domestic and international travel have increased transmission, across highly connected cities such as Singapore. The low seroprevalence rates across the national population make the implementation of vaccination using Dengvaxia (CYD-TDV) challenging, techniques such as Wolbachia, fogging and breeding site reduction are utilized to both prevent and control epidemics [4]. The successful application of these methods in epidemics depends on the correct timing for control ramp up in which house inspections increase, community engagement campaigns are rolled out to generate awareness in breeding site reduction and fogging in areas deemed at high risk of transmission [5].The characterization of dengue transmission dynamics through time is critical. Finite resources for ramp up of vector control measures beg the question of estimating the duration and severity of epidemics in different climates
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