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 humans
and minks.