  By the end of July 2020, the COVID-19 pandemic had infected more than
seventeen million people and had spread to almost all countries worldwide. In
response, many countries all over the world have used different methods to
reduce the infection rate, such as including case isolation, the closure of
schools and universities, banning public events, and mostly forcing social
distancing, including local and national lockdowns. We use a Monte-Carlo (MC)
based algorithm to predict the virus infection rate for different population
densities using the most recent epidemic data in our work. We test the spread
of the Coronavirus using three different lockdown models, and eight various
combinations of constraints, which allow us to examine the efficiency of each
model and constraint. In this paper, we have tested three different time-cyclic
patterns of no-restrictions/lockdown patterns. This model's main prediction is
that a cyclic schedule of no-restrictions/lockdown that contains at least ten
days of lockdown for each time cycle can help control the virus infection. In
particular, this model reduces the infection rate when accompanied by social
distancing and complete isolation of symptomatic patients.
