Abstract
The worldwide outbreak triggered by the SARS-CoV-2 coronavirus has emphasized the
pressing requirement for diverse mathematical models that can accurately depict the
patterns of how infections spread, especially considering the changing nature of viral
mutations. This study introduced a mathematical framework designed to simulate
the transmission dynamics of SARS-CoV-2, utilizing a system of nonlinear ordinary
differential equations (ODEs) to gauge the influence of newly developed mutations.
The proposed model took into account key epidemiological parameters to replicate the
overall spread of the virus in South Africa, extending its scope to include the dynamic
emergence and propagation of SARS-CoV-2 variants. By incorporating variant-specific
parameters enabled a better understanding of the interaction between viral evolution
and its effect on the virus infections. Validation of the model involved the use of
real-world data on SARS-CoV-2 strains, such as the ancestral strain and Beta variant,
as well as daily infection cases. This allowed for calibration and refinement through
model fitting. Upon analyzing the model and conducting numerical simulations, it
became evident that emerging variants significantly impact the disease progression of
SARS-CoV-2. The findings suggest that by minimizing the transmission level for the
second variant and lowering the reinfection level among people who have recovered
from the initial strain are crucial for controlling and potentially reducing SARS-CoV-2.
Furthermore, the model serves as a foundation for predicting the severity of emerging
variants, aiding in the progress of targeted public health interventions to lessen the
impact of evolving viral strains. Therefore, the utilization of an advanced mathematical
model is crucial in enhancing our comprehension pertaining to the complicated behavior
of SARS-CoV-2 infection, incorporating the influence from newly emerging variants.
Keywords: SARS-CoV-2, evolving, emerging variant, re-infections