Speaker
Description
Quantifying how fast pathogens spread across space is key to understanding epidemic dynamics and informing control strategies. Traditional approaches often rely on full phylogenetic reconstruction or spatially explicit models, which can be computationally demanding and sensitive to sampling biases between locations.
Here, we present a method to estimate the rate of geographical spread of pathogens between locations directly from pairwise genetic distance distributions, without requiring an explicit tree. The approach uses a continuous-time Markov chain model to link genetic divergence to spatial separation, enabling inference of the rate of spread from large genomic datasets. By leveraging summary statistics of pairwise distances, the method remains robust to geographically biased sampling and scales efficiently to thousands of sequences. It also provides a natural way to assess model fit.
Through a large simulation study, we demonstrate that the method accurately recovers known rates of pathogen spread across diverse epidemiological dynamics and sampling scenarios. Applying the method to >300,000 SARS-CoV-2 sequences sampled across Europe in 2020 shows its ability to capture spatial structure and temporal variation in the rate of pathogen spread, which are associated with air travel data.
This framework provides an exciting avenue to estimate rates of spread between locations in a robust and computationally efficient way, and explore drivers of spread at the population level.
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