Teddy: neural inference of epidemiological parameters from viral sequences
Garot, V.; Blassel, L.; Nesterenko, L.; ZHUKOVA, A.; Alizon, S.; Jacob, L.
Show abstract
Estimating how fast infections spread or how long they last is essential to control outbreaks. Phylodynamics methods enable the inference of key epidemiological pa-rameters from viral genomic data but remain limited in terms of biological realism and speed because they need to derive and compute likelihoods. We address this is-sue using simulation-based inference and introduce a deep learning-based framework directly trained on alignments of viral genetic sequences. Our neural posterior estima-tion matches the accuracy of leading Bayesian likelihood-based methods while running a thousand times faster and avoiding a phylogeny reconstruction step. This perfor-mance can be harnessed to analyze large datasets and opens new perspectives to tackle biologically realistic models in terms of pathogen life histories or genomic evolution.
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