COVID-19: Is your state getting better? Essential Metrics
and Graphs

Is your state getting better?

Is your state on this critical corona virus warning list?
→ Hint: it's NOT the list you're seeing in the news.

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This just In - 04/08/2020

The data feed is dead! Long live the data feed!

Our beloved, The COVID Tracking Project, ceased operations as of March 7th, 2021. This major project, propitiously and generously started by The Atlantic magazine, was the only comprehensive, publicly available source when the world was desperate to understand what was happening. New sources arose over the months, but TCTP was still a primary source for most of the places that provide data online.

Since the bracing news of TCTP's closure, we have been busy scouting out a new a new source of quality data, and adapting the precision tool that is, to display it to you. Our new source is John Hopkins University - Whiting Engineering School. The integration work was completed on Friday, April 9th. You are looking at it! (Well, if you scroll a little bit.) now running on Johns Hopkins University data!

What took so long? Unlike The COVID Tracking Project (TCTP), Johns Hopkins University (JHU) doesn't offer an API. It just provides CSV files, like spreadsheets of data for each day. You aren't expected to understand this, so it won't be on the quiz, but it means that required a significant rewrite to consume data in this form. In addition, there are significant differences in what actual data are provided. For example, TCTP provided daily changes, but no per capita numbers, while JHU provides provides no daily changes, but does provide per capita numbers. We had to add code to do calculations no longer included in the data.

There will be a few changes in our data:

TCTP manually aggregated hospitalization numbers, and processed them to provide a workable output. In fact, JHU as well as many other data web sites, used these numbers. Now, Hospitalization numbers are very sketchy and hard to come by, so we don't expect to have current (post 3/7) Hospitalization numbers for a while.

Test Percent Positives
There has been a significant change in this data as testing has become more and more available. TCTP was already struggling with how and what to present, as states have very different methodologies. These different methodologies actually change the meaning of the numbers, so comparing state to state was difficult to make meaningful.

TCTP created a system by which they picked different numbers for each state, so that they could be compared somewhat. JHU doesn't do this, so our Percent positive number will be useful in viewing a states performance over time, but may not be a good way to compare between states. We are still studying this, but for now, this is how they should be viewed. Read more about testing on the TCTP site: Test Types 101: The Pros and Cons of Different Approaches.

As our users are aware, we've been big advocates of focusing on Percent Positives over "Cases" as the best way to see trends independent of how well each state is doing with testing.

Fortunately, as testing has become institutionalized in every state, testing has become much more consistent, and case numbers have become much more reliable. Hence, Percent positives, are no longer as critical in determining how a state is faring.
Now: Johns Hopkins data!
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Why? Why, another COVID data page?

The Data - Basic Page - (for more complex data go to our Advanced Page)
( All line graphs smoothed using a -day time average)

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Click a filter then click a set or select specific states: click in the field and start typing state name or abbreviation. Click matching items to add to the list.

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% Positives:

Percent Positives - Daily

Down is good. This section shows the percentage of new tests that are positive. In 2020, THIS IS THE IMPORTANT NUMBER because it most directly indicated the actual rate of change in INFECTIONS, rather than cases. With the advent of multiple types of tests, and methodologies, this number is harder to use when comparing state to state. However, case numbers below are now more reliable.

Percent Positive Tests < 5% for at least a week is one requirement cited by some authorities for reducing quarantine restrictions.
Positive Tests (%)

Cases - Daily / 100k

These are the new cases reported daily per 100k population.

What's wrong with this metric? A state can make horrible Case numbers look good by doing less testing! Also, it punishes good policies, e.g. more testing, by making Case numbers look worse. However, with institutionalized and ubiquitous testing, this number is much more reliable than it was in 2020.

How? Say 40% the population has C-19, and we do 20 tests today. Then, we will get 8 New Cases today (40% of 20 tests). Now, say infection goes up to 50%, but we only do 10 tests today. Then we will get 5 New Cases today (50% of 10 tests). The infection rate is rising, but the New Cases NUMBER (which is the one we see the most in the news) has dropped by a lot, because testing has dropped a lot.

Percent Positives, would say we had 40% yesterday, and 50% today, so infection is going up. Period.

See Percent Positives above for a better indicator of infection rates. Deaths and Hospitalizations are also a better indicator of infection, than "cases".
Cases / 100k

Hospitalizations - Daily / 100k

Down is good. This shows the daily change in hospitalizations per 100k population. Hospitalizations data from many states is very low quality, with many states reporting irregularly, and some states not at all. This results in weird looking graphs, but you can get the gist for most states. We have no source for this data past 3/7/21.
Hospitalizations / 100k

Deaths - Daily / 100k

Down is good. In conjunction with the other metrics, this can give an indication of how well a state's healthcare community is coping with the case load.
Deaths / 100k

Tests - Daily / 100k

Up is good. If the daily Cases graph is climbing, check here to see if the Tests is also climbing. If so, then the increase in cases may reflect more existing infections being detected, and not an increase in infection. Conversely, if a state is a "New Case Winner", but their testing is going down, they may just have infections that are not being counted.
Tests / 100k

Cases - Cumulative / 100k

Flat is good. If the curve was steep and is now leveling that is good. If it has been shallow or level and is now climbing, that may not be good depending on the testing rate.

*Raw data on which this page is based is pulled, daily, from the Johns Hopkins University, Whiting School of Engineering GIT repository.
Tables, charts and additional processing of data provided by The Last Lemming, a division of CamRock Interactive, Inc.
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