Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The COVID-19 pandemic exemplified the need for a rapid, effective genomic-based surveillance system to predict emerging SARS-CoV-2 variants and lineages. Traditional molecular epidemiology methods, which leverage public health surveillance or integrated sequence data repositories, are able to characterize the evolutionary history of infection waves and genetic evolution but fall short in predicting future outlooks in promptly anticipating viral genetic alterations. To bridge this gap, we introduce a novel Deep learning, autoencoder-based method for anomaly detection in SARS-CoV-2 (DeepAutoCov). Trained and updated on the public global SARS-CoV-2 GISAID database. DeepAutoCov identifies Future Dominant Lineages (FDLs), defined as lineages comprising at least 25% of SARS-CoV-2 genomes added on a given week, on a weekly basis, using the Spike (S) protein. Our algorithm is grounded on anomaly detection