  A simple analytical model for modeling the evolution of the 2020 COVID-19
pandemic is presented. The model is based on the numerical solution of the
widely used Susceptible-Infectious-Removed (SIR) populations model for
describing epidemics. We consider an expanded version of the original
Kermack-McKendrick model, which includes a decaying value of the parameter
$\beta$ (the effective contact rate) due to externally imposed conditions, to
which we refer as the forced-SIR (FSIR) model. We introduce an approximate
analytical solution to the differential equations that represent the FSIR model
which gives very reasonable fits to real data for a number of countries over a
period of 100 days (from the first onset of exponential increase, in China).
The proposed model contains 3 adjustable parameters which are obtained by
fitting actual data (up to April 28, 2020). We analyze these results to infer
the physical meaning of the parameters involved. We use the model to make
predictions about the total expected number of infections in each country as
well as the date when the number of infections will have reached 99% of this
total. We also compare key findings of the model with recently reported results
on the high contagiousness and rapid spread of the disease.

Populations and Evolution