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Yucai Shao

Publications and source records attributed to Yucai Shao.

2 recordsLinked to original sources
PubMed2026

Unraveling the epidemiological and dispersal dynamics of the 2024-2025 chikungunya virus epidemic on Réunion Island.

Réunion Island experienced a massive chikungunya virus epidemic in 2024-2025, with >54,000 confirmed cases. This is the second major chikungunya epidemic on the island, following the first one that peaked 20 years ago. It has been asserted that this new outbreak finds its origin in a single introduction event into the island, offering an opportunity to exploit viral genomic data to understand the epidemiological and dispersal dynamics of the introduced transmission chain. We sequenced >3,000 viral genomes collected during the epidemic. Harnessing this genomic dataset, we used several phylogeographic and phylodynamic approaches to unravel the paths taken by the transmission chain and the external factors that might have impacted its dispersal and epidemiological dynamics on the island. Our analyses highlight a dispersal pattern in line with a gravity-model dynamic with viral transition events being more frequent from and toward more populated areas. Our analyses reveal that the transmission chain was overall spatially intermixed, with frequent exchanges among residential areas. In addition, we show that the temporal dynamic and intensity of the epidemic were associated with climatic variables, namely temperature and precipitation. Our results also show that in theory, the population immunity-resulting from this epidemic and the previous one (2005-2006)-could be sufficient to explain on its own the decrease in the transmission rate that led to the end of the epidemic. While a short-term resurgence cannot be excluded, the risk of a large-scale circulation of the virus in the human population appears therefore relatively limited in the upcoming seasons.

Reunion↗
PubMed2025

Parallel algorithms for phylogenetic inference under a structured coalescent approximation.

While advances in molecular epidemiology and computational modeling have enhanced our capacity to track pathogen evolution, the accurate reconstruction of spatiotemporal transmission dynamics remains essential for developing epidemic preparedness frameworks and implementing outbreak response measures. Structured coalescent models offer a phylogeographic framework by restricting lineage coalescence events to geographically proximate host populations. Although the Bayesian structured coalescent approximation (BASTA) provides a tractable approach, contemporary phylogeographic analyses involving dozens of geographic localities and hundreds to thousands of viral genomes substantially exceed the computational capacity of existing implementations. The BASTA likelihood scales cubically with deme count and quadratically with sequence count due to matrix exponentiation and pairwise coalescent probability calculations. Here, we introduce a comprehensive algorithmic restructuring of the structured coalescent likelihood that eliminates redundancies, optimizes memory access, and exposes parallelization opportunities. Our approach reorganizes computations along three dimensions: (i) independent calculation of deme-transition probability matrices across time intervals; (ii) simultaneous evaluation of partial likelihood vectors within temporal slices; and (iii) concurrent aggregation of coalescent probabilities. Algorithmic restructuring cuts average coalescent likelihood computation by 7-8 fold, and parallelization further boosts performance to 10-26 fold, enabling joint phylogeographic analyses of dengue virus across 10 South American countries and H5N1 avian influenza across 20 Eurasian regions to finish in a fraction of prior time. This computational efficiency also enables comparison between backward-in-time structured coalescent approximations and forward-in-time phylogeographic methods, revealing that the former provides appropriately conservative posterior estimates, particularly at intermediate phylogenetic depths. We integrate our implementation into the popular BEAST X and BEAGLE software packages, with an accompanying interface in BEAUti X to easily set up the analyses, providing researchers with an accessible and scalable tool for real-time phylogeographic surveillance of rapidly evolving pathogens.

Journal Article↗