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Explore graphics made with the COVID Tracking Project dataset along with tips to help you present the data in the clearest and most accurate way possible.

Complete, up-to-date testing and outcomes data is essential to a successful public health response to the US COVID-19 outbreak. For months, we’ve worked to patch together inconsistent state-reported data into a national set of numbers for COVID-19 case, death, and testing in the US with full daily updates.

Because COVID-19 testing and reporting are inconsistent among states, it’s easy to misinterpret the data. That makes it especially important to create clear and accurate visualizations. Otherwise even simple and minimalistic graphics can be misleading. If you plan to display data from the COVID Tracking Project yourself, please closely follow these design and visualization guidelines.

Consider normalizing the data.

If you’re creating a choropleth map (where each state is shaded in proportion to a statistical variable), make sure you encode a population-controlled rate, such as “positive tests per one million people.” If you want to show absolute numbers, such as the number of new positive cases per day, use a symbol map.

The Spread of COVID-19 in the US

Nov 23

0 total tests
0 positive tests
Mar 2
Nov 23
Bubble MapChoropleth Map

*Per one million people

Choose colors carefully.

Readers are likely experiencing some latent anxiety, so do your best to neither make light of the situation nor be alarmist about it. One application of this is in your color choice: You don’t want your map’s color scheme or design to minimize the situation by being overly playful or lighthearted. You also don’t want to select colors that suggest the worst possible outcome.

Include the denominator.

Testing is one of the most important tools in controlling an outbreak. When universal testing is implemented, people who are infected with the virus can be isolated from folks who test negative. This functions as a targeted social distancing technique and can help slow the outbreak.

Charting the number of positive tests alone is often problematic. Simple case counts show where people are being tested, not necessarily where people are sick. To illustrate the point, a state that reports three cases of COVID-19 after testing 2,000 people is probably in a different stage of its outbreak than a state that reports three cases but has only tested 20 people. But if all you have is a case count, those states look exactly the same. That is why we need to include the total number of tests as a denominator.

Positive tests and total tests in the US

  • Positive tests
  • Total tests

A note on total tests: in the early months of the pandemic, we calculated this figure by adding together positive and negative tests reported by states. But as data reporting evolved, we started using total test numbers published directly by states, a figure often reported in different units. Though we are publishing total test numbers in all available units for each state and territory on our website, and in separate fields in our API, we prioritize units of test encounters and specimens above people for calculating our test totals. We lay out all the methodology details on our total tests explainer.

If you use total tests in your visualizations, be mindful to note different units when comparing states or time periods, and add disclaimers when necessary.

Be mindful when comparing states.

By comparing positive tests to total tests in each state and territory, we can get a sense of how widespread a state’s testing regime might be (while keeping in mind that population densities vary widely across the country).

Cumulative tests by state

Display by:
Total testsTests per capita*