Statistics with Sasa

Unless you live under a rock, you've heard of COVID-19, colloquially known as Coronavirus (or, if you prefer,  Caronavirus ). Your state may have multiple confirmed cases, and you may be under quarantine and working from home. You might even have it and passed it to others in your life . In any way, it has undoubtedly affected your life at this point as it has taken the world by storm: Perhaps the headline of Coronavirus in the United States has been the Federal Government's alleged mishandling of the virus as it reached our shores. From faulty test kits to contradicting statements from our Commander-in-Chief, it's hard to say that the response has been stellar. I wanted to see if we can statistically show that the government's conduct in these trying times has had a measurable impact on outcomes for U.S. residents. First, I'll start off by listing reasons why this exercise is a fair one and not some political witch-hunt. In the lead-up to the pandemic, the

To create my metric, I make use of a 2-parameter model pioneered by Viboud et. al . Specifically, it measures number of cases on day  t, C(t),  using the following parameters:  r  is a measure of growth rate,  A  is a constant, and the "deceleration of growth parameter" is given by $p = 1-\frac{1}{m}$, where  p  is between 0 and 1. Specifically,  p  has unique properties. If  p  is equal to 0, then the cumulative number of cases grows  linearly.  If  p  is equal to 1, then the cumulative number of cases grows  exponentially.  Everything in-between displays  sub-exponential  growth. Cases with sub-exponential growth can be modeled using the following formula: $C(t)=(\frac{r}{m}t+A)^{m}$ r  and  p  can be estimated by applying current data and nonlinear least squares (NLS) to the above formula. In practice, as  p  approaches 0, a plot of cumulative cases is better represented by a linear model than an exponential one. Conversely, as  p  approaches 1, a plot of cumulati