  In this paper, a susceptible-infected-removed (SIR) model has been used to
track the evolution of the spread of the COVID-19 virus in four countries of
interest. In particular, the epidemic model, that depends on some basic
characteristics, has been applied to model the time evolution of the disease in
Italy, India, South Korea and Iran. The economic, social and health
consequences of the spread of the virus have been cataclysmic. Hence, it is
essential that available mathematical models can be developed and used for the
comparison to be made between published data sets and model predictions. The
predictions estimated from the SIR model here, can be used in both the
qualitative and quantitative analysis of the spread. It gives an insight into
the spread of the virus that the published data alone cannot do by updating
them and the model on a daily basis. For example, it is possible to detect the
early onset of a spike in infections or the development of a second wave using
our modeling approach. We considered data from March to June, 2020, when
different communities are severely affected. We demonstrate predictions
depending on the model's parameters related to the spread of COVID-19 until
September 2020. By comparing the published data and model results, we conclude
that in this way, it may be possible to better reflect the success or failure
of the adequate measures implemented by governments and individuals to mitigate
and control the current pandemic.
