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
Jan 19
*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
* Per capita = per one million people
Don’t ignore data uncertainty.
Though this is a national crisis, each US state or territory reports its data differently. We track numbers provided by each state, but the quality and frequency of reports vary widely. Transparency is crucial. Be honest about what is in the data that you are charting and what isn’t. Footnotes and annotations can help you make these disclaimers. We publish details about inconsistencies in data reporting for every state and territory.
Use absolute numbers for death counts.
An organized, collective, and timely response from the government and other authorities is a key factor in saving lives. One metric you can use to measure the response’s effectiveness is the number of deaths attributed to the virus per day and/or per state or territory.
We recommend using total numbers for plotting deaths to compare one US state or territory against another. In this case, adjusting per capita adds a layer of abstraction to the graphic. This reduces the data’s power and the reader’s comprehension. It’s easier to picture 200 fatalities than 0.0001 fatalities per capita, as John Burn-Murdoch, a data journalist at the Financial Times, pointed out.
Daily deaths in the US
Total Deaths By States
Remember that even death counts are uncertain.
Data that tracks COVID-19 death counts is still a gray area. Though some experts prefer to measure the pandemic’s severity using the number of deaths instead of total cases, several factors could bias COVID-19 mortality data.
There are some concerns that official death statistics may overcount COVID-19 fatalities by assuming any patient who tests positive for the virus was killed by it. But Marc Lipsitch, a Harvard University epidemiologist, told FactCheck.org that “the number of such cases will be small.” Undercounting is a bigger problem, he says. “A greater issue is errors in the other direction.”
If people die from COVID-19 before they are tested, their death might not be included in the official tally. For example, a WNYC/Gothamist investigation found that as of April 7, around 200 New Yorkers had died at home every day without access to testing and medical treatment. That’s 10 times higher than NYC’s typical at-home death rate. These deaths are likely caused by COVID-19. If this effect is widespread in the United States, that means official statistics undercount the disease’s fatality rate.
To get better grounding when interpreting death rates, consider comparing COVID-19 mortality rates for a given location since the beginning of the outbreak to fatalities during the same period in previous years.
Be clear and honest.
While news is moving faster than ever to keep up with the pace of the pandemic spread, designers and visualization experts’ goal is to present COVID-19 data in a clear and honest way. Provide context, and consider the tips above to avoid common pitfalls in data reporting as we seek to inform people during this time of crisis.