Characterising patterns of infectious disease transmission from identical pathogen sequences
Pathogen genomics has become an important tool to characterize how infectious diseases spread throughout populations. Existing approaches however suffer from two limitations. First, they do not scale well to large pathogen genome datasets. Second, heterogeneity in sequences can largely bias results from phylodynamics analyses.
In this talk, I will present work I’ve done to overcome these limitations by analysing identical pathogen sequences. First, I will show how we can use the locations in which pairs of identical sequences are collected to characterize fine-scale disease transmission patterns. I will illustrate this approach by analyzing 114,298 SARS-CoV-2 sequences collected through Washington State genomic sentinel surveillance program, for which detailed metadata (county and postal code of home location, age group) is available. Second, I will present a method based on the size of clusters of identical pathogen sequences, which we can use to infer the parameters of the offspring distribution (reproduction number R and dispersion parameter k).
These two methods do not rely on inferring a pathogen’s phylogenetic tree and therefore provide valuable new tools to leverage large-scale pathogen genome datasets such as those that have been generated during the COVID-19 pandemic.
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