Testing Hub

Testing Trends Tool

Track Trends in COVID-19 Cases and Tests

About the Data

  • New cases are presented as daily counts as reported by the state; for smoothed data presented as a 3-day rolling average, click here. Due to fluctuations in daily reporting, testing rates are presented as 7-day rolling averages.
  • As guidance evolves on Covid-19 case reporting, some states are modifying their reporting to include both confirmed cases, based on laboratory testing, and probable cases, based on specific criteria for symptoms and exposure reflect. This may cause new case data to "spike."
  • It is important to note that the quality of testing data varies by state. Click here for more.

Data Sources: Testing data from The COVID Tracking Project and cases data from JHU CSSE.

Conceptualized by: International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health (www.jhsph.edu/ivac/): Melissa Higdon, Maria Deloria Knoll, Maria Garcia Quesada, Julia Bennett

About this page:

This page was last updated on Wednesday, September 23, 2020 at 03:00 AM EDT.

These charts lay out the key metrics for understanding the reach and severity of COVID-19 in a given area: number of new daily cases, tests per 100,000 people (testing rate), and percentage of tests that are positive (positivity rate).

As testing capacity increases, considering confirmed new cases, testing rates, and percent positivity together gives us a fuller picture of COVID-19 in a particular state or region. Under these conditions and stable testing practices, trends in daily cases can be cautiously interpreted as trends in transmission of the virus. Leaders can then make informed decisions about lifting social distancing and other transmission control measures.

More Details
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Positivity Rates: Our calculation, which is applied consistently across the site and predates most states’ test positivity tracking efforts, looks at number of cases divided by number of negative tests plus number of cases. We feel that the ideal way to calculate positivity would be number of people who test positive divided by number of people who are tested. We feel this is currently the best way to track positivity because some states include in their testing totals duplicative tests obtained in succession on the same individual, as well as unrelated antibody tests. However, many states are unable to track number of people tested, so they only track number of tests. Because states do not all publish number of positive and number of negative tests per day, we have no choice but to calculate positivity via our approach. We describe our methodology as well as our data source (COVID Tracking Project) clearly on the site.

7-Day Averages: The CRC calculates the rolling 7-day average separately for daily cases and daily tests, and then for each day calculate the percentage over the rolling averages. Some states may be calculating the positivity percentage for each day, and then doing the rolling 7-day average. The reason why we use our approach is because testing capacity issues and uneven reporting cadences create a lot of misleading peaks and valleys in the data. Since we want to give a 7-day average, it is more fair to average the raw data and then calculate the ratios. Otherwise, days when a large number of negative tests are released all at once—and positivity is going to be very low—will have the same weight as days when data was steadily released, and the overall result is going to be lower. Our approach is applied to all our testing data to correct for these uneven data release patterns.

Positivity rates can tell us whether a state’s testing capacity is sufficient. Ideally, a state should be meeting or exceeding the recommended positivity rate, which the WHO has set at 5%. A positivity rate over 5% indicates a state may only be testing the sickest patients who seek out medical care, and are not casting a wide enough net to identify milder cases and track outbreaks.

Percent positivity can also help us determine if an increase in cases is simply the result of expanded testing or if it signals increased transmission of the virus. If we see the percentage of positive tests begin to rise, it indicates insufficient testing to find infections that may be occurring. Not finding these infections may mean that the virus is transmitting without intervention, which can lead to future case growth.

Specifically:

  • If a rise in cases is the result of increased testing, the percent positive line could look flat or like it is decreasing over the time period when cases increased.

  • If a rise in cases is the result of increased transmission, the line could appear to be increasing over that same time period.

How we calculate positivity
arrow-rightCreated with Sketch.

Positivity Rates: Our calculation, which is applied consistently across the site and predates most states’ test positivity tracking efforts, looks at number of cases divided by number of negative tests plus number of cases. We feel that the ideal way to calculate positivity would be number of people who test positive divided by number of people who are tested. We feel this is currently the best way to track positivity because some states include in their testing totals duplicative tests obtained in succession on the same individual, as well as unrelated antibody tests. However, many states are unable to track number of people tested, so they only track number of tests. Because states do not all publish number of positive and number of negative tests per day, we have no choice but to calculate positivity via our approach. We describe our methodology as well as our data source (COVID Tracking Project) clearly on the site.

7-Day Averages: The CRC calculates the rolling 7-day average separately for daily cases and daily tests, and then for each day calculate the percentage over the rolling averages. Some states may be calculating the positivity percentage for each day, and then doing the rolling 7-day average. The reason why we use our approach is because testing capacity issues and uneven reporting cadences create a lot of misleading peaks and valleys in the data. Since we want to give a 7-day average, it is more fair to average the raw data and then calculate the ratios. Otherwise, days when a large number of negative tests are released all at once—and positivity is going to be very low—will have the same weight as days when data was steadily released, and the overall result is going to be lower. Our approach is applied to all our testing data to correct for these uneven data release patterns.