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Testing Hub

Testing Trends Tool

Track Trends in COVID-19 Cases and Tests

Data Sources: Case data from JHU CSSE. As of March 3, 2021, testing data is drawn from JHU CCI. Prior to that, the data source was the The COVID Tracking Project.

Learn more about why the positivity rates shown on our site may differ from state calculations

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 Friday, July 1, 2022 at 06:01 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).

Positivity rates are a measure of testing capacity, and can help gauge whether governments are casting a wide enough net with their testing programs to identify infections that may be occurring. While this metric can provide important context about case totals and trends, it is NOT a measure of how prevalent the virus is in communities. Policy decisions, like openings and closings or interstate travel, should not be determined based on test positivity alone. Considering confirmed new cases, testing rates, and percent positivity together gives us a fuller picture of COVID-19 in a particular state or region.

More Details
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Click here to learn more about why the positivity rates shown on our site may differ from state calculations

7-Day Averages: The CRC calculates the rolling 7-day average separately for each daily numerator and denominator data point, and then for each day calculates 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. We use our approach because testing capacity issues and uneven reporting cadences create many misleading peaks and valleys in the data. Since we want to give a 7-day average, it is fairer to average the raw data and then calculate the ratios. Otherwise, days when a large number of negative tests are released at once—resulting in very low positivity—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 the release of uneven data.

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.

Click here to learn more about why the positivity rates shown on our site may differ from state calculations

7-Day Averages: The CRC calculates the rolling 7-day average separately for each daily numerator and denominator data point, and then for each day calculates 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. We use our approach because testing capacity issues and uneven reporting cadences create many misleading peaks and valleys in the data. Since we want to give a 7-day average, it is fairer to average the raw data and then calculate the ratios. Otherwise, days when a large number of negative tests are released at once—resulting in very low positivity—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 the release of uneven data.