The point of this page is not necessarily to cast blame on NY, NYC, or any of its politicians in particular, but rather to show how the data construed a certain way can shape the narrative to be used either in the defense or to attack individuals or policy decisions.

#### Hospitalized By State

###### About this graph:

The number of people hospitalized by COVID-19 gives us one of the more accurate pictures of how an area was effected by the virus. For one, it is reflective of the general severity with which the region was hit, much better than just testing since that can vary based on type of testing, false positives/negatives, testing availability, or asymptomatic cases. Since the virus is more dangerous for different demographics (esp. ages 65+) and those with comorbidities, hospitalizations can serve as a cheap stand-in for the types of people being effected at a given time and how a given region may have been able to protect the more vulnerable populations.

Please note that it appears not all states started tracking this data at the same time and so some (in particular Florida) start much later in timeline.

#### Daily Fatality Increase By State (per 100k pop)

###### About this graph:

Fatalities similarly can tell us more about the nature of how a region was hit more than just cases and how those in charge of policy were able to respond. To give an exaggerated example, if in a group of 1,000 individuals, 90% test positive, but only half show any symptoms and only 2 of those ends up hospitalized or a fatality, this has much different policy implications than if 10 or 20 do, and it paints a much different picture of how this group was effected.

#### Unemployment Rate

###### About this graph:

This is a stacked area chart. The height of each state's unemployment graph doesn't represent its absolute value but rather they are laid on top of each other to make it easier to compare their relative sizes over time. For example, on May 5th, NY had an employment rate of 19.6% while TX had a rate of 9.6% and you'll notice that the space for NY in the graph is larger than that for TX (its height on the graph is irrelevant).

The chart's areas are sorted by average unemployment rate over the period of time, with the top state having the highest average and the bottom having the lowest.

Together with fatality and hospitality rates, a graph like this *could* be used to roughly judge the effects and efficacy of state level policies.

##### Average Unemployment Rates (Mar 23 - Mar 1)

#### Total Fatalities (per 100k)

###### About this graph:

Sometimes just showing trend lines doesn't tell us the whole story. While the situation on the ground may change over time and useful to look at in many situations, aggregate numbers are also helpful in evaluating overall performance. This is especially true considering that environmental and transmission variables can effect when an area is hit.

It's also important to note that the total *relative to population* is the measure we want here. 10 fatalities in a group of 100 (10%!) is very different then in a group of 10,000 (.1%).

This data set uses the Johns Hopkins University (JHU) value for totals as there are some discrepencies from state level reporting. In particular, JHU counts over 7k more reported fatalities for totals than what is captured by the COVID Tracking Project.

Data last updated: Mar 7, 2021

#### Estimated Cases (based on 0.65% IFR)

###### About this graph:

Given that available statistics telling us the amount of positive COVID-19 cases is not constant over time, and early on in particular available testing capacity was far lower than by May, 2020, the total case count doesn't give us a very accurate picture of how many cases actually hit a region.

A closer if imperfect alternative way to calculate this is by using the IFR or Infection Fatality Rate, which gives us an estimate of how many positive cases end up resulting in a fatality. By dividing the number of fatalities for a given day by the IFR, we can estimate the *actual* number of infected approximately 15 days prior assuming. So if there are 100 fatalities on day 100, we can assume that on day 85 there were approximately 153,846 people infected (100 / .0065).

An IFR of 0.65% is assumed based off of the estimates provided by the CDC.

This graph can be compared to the unadjusted graph that uses raw positive case count in the NY Beat COVID-19.