Day 187

Coronavirus Lingo - How is the Positivity Rate defined and calculated?

Positivity Rates:  The John Hopkins University (JHU) 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. They feel that the ideal way to calculate positivity would be number of people who test positive divided by number of people who are tested. They 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, they have no choice but to calculate positivity via our approach. We describe our methodology as well as their data source (COVID Tracking Project) clearly on the site. Click here for more info.

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 JHU uses their 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. The JHU approach is applied to all their testing data to correct for these uneven data release patterns.

https://coronavirus.jhu.edu/testing/tracker/overview