See daily changes in tests performed and positivity rates
Track changes in the number of administered tests and positivity rates. To narrow the view of the larger chart drag the horizontal arrows bookending the smaller gray bar beneath it.
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 Friday, July 1, 2022 at 06:01 AM EDT.
This graph shows the total daily number of virus tests conducted in each state and of those tests, how many were positive each day. The trend line in blue shows the average percentage of tests that were positive over the last 7 days. The rate of positivity is an important indicator because it can provide insights into whether a community is conducting enough testing to find cases. If a community’s positivity is high, it suggests that that community may largely be testing the sickest patients and possibly missing milder or asymptomatic cases. A lower positivity may indicate that a community is including in its testing patients with milder or no symptoms. The WHO has said that in countries that have conducted extensive testing for COVID-19, 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.
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.