Speaker
Description
Singapore faces recurring dengue outbreaks that pose substantial public health and economic burdens, with recent years seeing unprecedented case numbers. While current surveillance systems track case counts, the ability to predict outbreaks in real-time remains challenging. We propose combining real-time estimation of the effective reproduction number (Rₑ) with analysis of viral spread patterns to enable earlier outbreak prediction and understand transmission dynamics.
We compare two approaches for estimating Rₑ: a traditional method using case counts with generation and incubation periods, and the Episodic Birth-Death-Sampling (EBDS) model which incorporates both phylogenetic and case data. The EBDS approach can potentially capture the impact of new variants and genetic diversity on transmission dynamics, which may not be evident from serotype case data alone. Using historical dengue case and sequencing data from the National Environment Agency (NEA), we assess each method's ability to correctly predict increases or decreases in case numbers, with particular attention to periods where Rₑ greatly exceeds 1. In parallel, we analyze the dengue virus sequences linked to postal codes to track the movement of viral variants. Bayesian phylogenetic inference using BEAST reconstructs viral spread within Singapore's districts and identifies international origins of variants associated with higher transmission rates.
This approach enables real-time prediction of case growth or decline while simultaneously providing insights into local transmission patterns and international viral introductions. The integration of Rₑ prediction with spatial analysis of viral spread improves our understanding of outbreak dynamics and shows enhanced predictive power compared to traditional surveillance approaches. These findings can consequently strengthen early warning systems for dengue outbreaks. This integrated methodology enables more timely and geographically targeted public health interventions, potentially improving outbreak control efforts.
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