The COVID-19 pandemic has introduced to
mild the risks of deadly epidemic-prone illnesses sweeping
our globalized planet. The pandemic is still going strong,
with additional viral variations popping up all the time. For
the close to future, the international response will have to
continue. The molecular tests for SARS-CoV-2 detection
may lead to False-negative results due to their genetic
similarity with other coronaviruses, as well as their ability
to mutate and evolve. Furthermore, the clinical features
caused by SARS-CoV-2 seem to be like the symptoms of
other viral infections, making identification even harder.
We constructed seven hidden Markov models for each
coronavirus family (SARS-CoV2, HCoV-OC43, HCoV229E, HCoV-NL63, HCoV-HKU1, MERS-CoV, and SARSCoV), using their complete genome to accurate diagnose
human infections. Besides, this study characterized and
classified the SARS-CoV2 strains according to their
different geographical regions. We built six SARS-CoV2
classifiers for each world's continent (Africa, Asia, Europe,
North America, South America, and Australia). The dataset
used was retrieved from the NCBI virus database. The
classification accuracy of these models achieves 100% in
differentiating any virus model among others in the
Coronavirus family. However, the accuracy of the continent
models showed a variable range of accuracies, sensitivity,
and specificity due to heterogeneous evolutional paths
among strains from 27 countries. South America model was
the highest accurate model compared to the other
geographical models. This finding has vital implications for
the management of COVID-19 and the improvement of
vaccines