Total Fatalities per 100k Pop.

US Adjusted w/o NY and NJ
About this graph:

For this page we include a value called "US Adjusted" which displays the US totals without those from the worst hit areas in the country (NY and NJ).

The "adjusted" value here helps to highlight a point with regards to narrative crafting which is that even when adjusting for population, there are some very major differences when the numbers (population, area, etc...) get really big. NYC and by extension NJ (as well as other surrounding areas) got hit by the Coronavirus incredibly hard for a variety of a reasons not all of which were within the control of policy makers (population density, transportation, volume of international travel, etc.). But if we want to make the claim that the United States as a country handled COVID-19 uniquely poorly then surely we'd expect that relative fatality numbers across the country, even without the hardest hit areas would still look bad.

As it turns out, this isn't what we see. Despite the data still including some of the largest cities in the country and other areas that got hit almost as hard, e.g. MA and CT, the Adjusted US number compares favorably with similar European countries.

This should indicate that COVID-19 outcomes are much more likely tied to circumstances, more of a regional, timing, and density phenomenon, then a policy one. It would be a tenuous claim to say the U.S. mishandled its COVID-19 response if a country of 302 million (once you've discounted NY and NJ populations) measures up favorably against the responses and outcomes of other countries.

Data last updated: Jul 29, 2021

Daily New Cases vs. Fatalities -
United States

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(per mil.)

About this graph:

This is a composed, bi-axial graph (two Y axes). The left reflects the value for case count, represented by the line while the right is for fatalities (the bars). Change the country you'd like to view the comparison of by selecting from the menu in the title.

If you're curious why the data is "smoothed" and what that means, check out the FAQ page.

There are a few useful things we can learn from this chart. First, case increases are not necessarily signs of things getting worse, especially since testing capacity and sensitivity changes over time. Many of the countries listed demonstrate a second spike of new cases, however none seem to have a commensurate increase in fatalities, which is, along with hospitalizations, the most important item to track and try and minimize from a policy standpoint.

Notice also for the U.S. that while we had regional first waves spread across the timeline, which is also shown here, fatalities only spiked once, and never reached the numbers (per million) as the other countries (except maybe Sweden). This is desireable from the standpoint of possible immunity as well as protecting and maintaining hospital capacity.

Daily New Cases (per million)

Cumulative Fatalities Over Time By Country (per mil.)

About this graph:

Here we can see the value in comparing different datasets in relative terms vs. absolute. This graph tells a very different story than that of the cumulative fatalities in absolute terms. You can flip the switch to see how the narrative changes.

New Tests

About this graph:

Tests give us a limited view into government response. This gives us the opportunity to see if a sufficient infrastructure was put into place to run systems such as a test and tace regime.