On May 12, 2020 the World Health Organization (WHO) advised governments that before reopening, rates of positivity in testing (ie, out of all tests conducted, how many came back positive for COVID-19) of should remain at 5% or lower for at least 14 days.
This initiative relies upon publicly available data from multiple sources. States are not consistent in how and when they release and update their data, and some may even retroactively change the numbers they report. This can affect the percentages you see presented in these data visualizations. We are taking steps to account for these irregularities in how we present the information, but it is important to understand the full context behind these data.
3/24 Note: Previous spikes in historical data for total and positive tests in the graphic were anomalies caused by the shift in data collection that began March 3 when the Coronavirus Resource Center (CRC) began obtaining data from the Johns Hopkins Centers for Civic Impact rather than from the COVID Tracking Project (CTP), which ceased operations March 7. The CRC also now includes non-resident tests in Alaska and Florida and probable cases in Hawaii.
It is important to track the testing that states are doing to diagnose people with COVID-19 infection in order to gauge the spread of COVID-19 in the U.S. and to know whether enough testing is occurring. When states report the number of COVID-19 tests performed, this should include the number of viral tests performed and the number of patients for which these tests were performed. Currently, states may not be distinguishing overall tests administered from the number of individuals who have been tested. This is an important limitation to the data that is available to track testing in the U.S., and states should work to address it.
When states report testing numbers for COVID-19 infection, they should not include serology or antibody tests. Antibody tests are not used to diagnose active COVID-19 infection and they do not provide insights into the number of cases of COVID-19 diagnosed or whether viral testing is sufficient to find infections that are occurring within each state. States that include serology tests within their overall COVID-19 testing numbers are misrepresenting their testing capacity and the extent to which they are working to identify COVID-19 infections within their communities. States that wish to track the number of serology tests being performed should report those numbers separately from viral tests performed to diagnose COVID-19.
This page was last updated on Monday, May 10, 2021 at 06:00 AM EDT.
If a positivity rate is too high, that may indicate that the state is only testing the sickest patients who seek medical attention, and is not casting a wide enough net to know how much of the virus is spreading within its communities. A low rate of positivity in testing data can be seen as a sign that a state has sufficient testing capacity for the size of their outbreak and is testing enough of its population to make informed decisions about reopening. Which U.S. states are testing enough to meet the WHO’s goal?
The graph below compares states’ rate of positivity to the recommended positivity rate of 5% or below. States that meet the WHO’s recommended criteria appear in green, while the states that are not testing enough to meet the positivity benchmark are in orange.
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.