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Real-time viral genome detection, taxonomic classification and phylogeneticanalysis are critical for efficient tracking and control of viral pandemicssuch as Covid-19. However, the unprecedented and still growing amounts of viralgenome data create a computational bottleneck, which effectively prevents thereal-time pandemic tracking. For genomic tracing to work effectively, each newviral genome sequence must be placed in its pangenomic context. Re-inferringthe full phylogeny of SARS-CoV-2, with datasets containing millions of samples,is prohibitively slow even using powerful computational resources. We areattempting to alleviate the computational bottleneck by modifying and applyingVision Transformer, a recently developed neural network model for imagerecognition, to taxonomic classification and placement of viral genomes, suchas SARS-CoV-2. Our solution, CoViT, places SARS-CoV-2 genome accessions ontoSARS-CoV-2 phylogenetic tree with the accuracy of 94.2%. Since CoViT is aclassification neural network, it provides more than one likely placement.Specifically, one of the two most likely placements suggested by CoViT iscorrect with the probability of 97.9%. The probability of the correct placementto be found among the five most likely placements generated by CoViT is 99.8%.The placement time is 0.055s per individual genome running on NVIDIAs GeForceRTX 2080 Ti GPU. We make CoViT available to research community through GitHub:https://github.com/zuherJahshan/covit.

CoViT: Real-time phylogenetics for the SARS-CoV-2 pandemic using Vision Transformers