  Within the likes of any highly contagious and unpredictable disease, lies a
predictable and attainable growth rate that researchers can find in order to
make logistical conclusions about that particular disease and its affected
regions' counterparts. The foundation that researchers pull from when studying
a particular disease and looking for its growth rate is the
Susceptible-Infected-Removed (SIR) model, presented by a series of differential
equations. The issue with the SIR model lies not in its complexity, but
actually its simplicity and lack of a potentially high-finite amount of
factors; the limit being bounded by the amount of data available for that
particular factor. Our research involves the application of multiple
regressions to pinpoint and identify our Covid lockdown periods, followed by
the modification of the SIR model. This involved creating new model
approximations such as the time-delayed SIR model and the reinfected SIR model
in order to take into account factors such as incubation and reinfection, and
get the lowest error discrepancy as possible for our infection rate. We were
able to conclude that the more factors that we took into account, our error
rate became lower and our results became more accurate. We could also identify
outlier Metros and draw certain conclusions on performance level and the
reasons behind them. We then moved on to find correlations, if any, between the
infection rates and outside factors. We looked at demographic and weather data
to demonstrate whether correlations appeared. We found that there are a few
factors with high correlations, including graduate education and low
temperatures.
