See daily changes in tests performed and positivity rates
Testing data from The COVID Tracking Project.;
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, September 21, 2020 at 03:00 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.
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.