  Simulation models for infection spread can help understand what factors play
a major role in infection spread. Health agencies like the Center for Disease
Control (CDC) can accordingly mandate effective guidelines to curb the spread.
We built an infection spread model to simulate disease propagation through
airborne transmission to study the impact of restaurant operational policies on
the Covid-19 infections. We use the Wells-Riley model to measure the expected
value of new infections in a given time-frame in a particular location. For the
purpose of this study, we have restricted our analysis to bars and restaurants
in the Minneapolis-St. Paul region. Our model helps identify disease hotspots
within the Twin Cities and proves that stay-at-home orders were effective
during the recent lockdown, and the people typically followed the social
distancing guidelines. To arrive at this conclusion, we performed significance
testing by considering specific hypothetical scenarios. At the end of the
study, we discuss the reasoning behind the hotspots, and make suggestions that
could help avoid them.
