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Marc A Suchard

Publications and source records attributed to Marc A Suchard.

4 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↗
PubMed2026

TreeFlow: Probabilistic Modelling and Automatic Differentiation for Phylogenetics.

Probabilistic modelling frameworks are powerful tools for statistical modelling and inference. They are not immediately generalizable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic modelling and automatic differentiation with phylogenetic trees. It embeds phylogenetic trees in the TensorFlow Probability framework, and implements inference algorithms for phylogenetic models given a fixed tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.

Bayesian inference↗
PubMed2026

Evolutionary history of Jamestown Canyon virus reveals complex multi-vector ecology.

Jamestown Canyon virus (JCV) is a historically understudied mosquito-borne virus of increasing concern in North America. We generated 658 whole-genome JCV sequences from northeast United States, including 84% (500/597) of all JCV-positive mosquitoes detected in Connecticut from 1997 to 2022. Then, we applied phylodynamic methods to demonstrate how mosquito phenology structures the maintenance and evolution of JCV. Our phylogenetic analyses estimate that JCV was introduced in the Northeast by at least the early 1700s, and the primary introductions of lineages A and B into Connecticut occurred during the mid-1800s to mid-1900s. Further, we estimate that JCV evolves at a rate of ∼3 × 10-5 substitutions per site per year (s/s/y), making it one of the slowest-evolving known RNA viruses, because the virus spends ∼10 months per year in evolutionary stasis while overwintering in mosquito eggs. To investigate ecological drivers of JCV spread in Connecticut, we paired discrete trait and continuous phylogeographic reconstructions with mosquito surveillance data. We estimate that JCV has a low diffusion rate of ∼30-60 km2/year, which is more similar to slow-moving tick-borne viruses than to other mosquito-borne viruses. We found that univoltine Aedes mosquitoes were likely to maintain the virus across years through overwintering in eggs, accounting for its slow evolution and dispersal, while multivoltine mosquitoes contributed to periodic bursts of spatial diffusion and amplification within seasons. We demonstrate the utility of dense sequencing and phylodynamics to disentangle complex transmission cycles, offering a framework for rapidly advancing our evolutionary and ecological knowledge of understudied viruses.

Animals↗
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↗