  In the last decade, humanity has faced many different pandemics such as SARS,
H1N1, and presently novel coronavirus (COVID-19). On one side, scientists are
focusing on vaccinations, and on the other side, there is a need to propose
models that can help us in understanding the spread of these pandemics as it
can help governmental and other concerned agencies to be well prepared,
especially from pandemics, which spreads faster like COVID-19. The main reason
for some epidemic turning into pandemics is the connectivity among different
regions of the world, which makes it easier to affect a wider geographical
area, often worldwide. In addition, the population distribution and social
coherence in the different regions of the world is non-uniform. Thus, once the
epidemic enters a region, then the local population distribution plays an
important role. Inspired by these ideas, we proposed a mobility-based SIR model
for epidemics, which especially takes into account pandemic situations. To the
best of our knowledge, this model is first of its kind, which takes into
account the population distribution and connectivity of different geographic
locations across the globe. In addition to presenting the mathematical proof of
our model, we have performed extensive simulations using synthetic data to
demonstrate our model's generalizability. To demonstrate the wider scope of our
model, we used our model to forecast the COVID-19 cases for Estonia.
