  In more than two years since the COVID-19 virus was first detected in China,
hundreds of millions of individuals have been infected, and millions have died.
Aside from the immediate need for medical solutions (such as vaccines and
medications) to treat the epidemic, the Corona pandemic has strengthened the
demand for mathematical models that can predict the spread of the pandemic in
an ever-changing reality. Here, we present a novel, dynamic particle model
based on the basic principles of statistical physics that enables the
prediction of the spreading of Covid-19 in the presence of effective vaccines.
This particle model enables us to accurately examine the effects of the vaccine
on different subgroups of the vaccinated population and the entire population
and to identify the vaccine waning. Furthermore, a particle model can predict
the prevalence of two competing variants over time and their associated
morbidity.
