Predicting reservoir hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic

The SARS-CoV-2 pandemic has raised the concern for reservoir hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features, to calculate the infection likelihoods and further predict the probable hosts of novel viruses. Overcoming the limitation of sequence similarity-based methods, DeepHoF was applied to the analysis of SARS-CoV-2 in the 2020 pandemic. Using the isolates sequenced in the earliest stage of COVID-19, DeepHoF identified minks, bats, dogs and cats can be highly susceptible to SARS-CoV-2, while minks might be one of the most noteworthy reservoir hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the infection likelihood on human or the host range. With a large-scale genome analysis based on DeepHoF’s computation for the later world-wide pandemic, it should not be slighted for the probably bidirectional transmission of SARS-CoV-2 between hu...