The fastest way to turnaround COVID-19 testing isn’t by running one sample at a time but by using a process that groups several samples together.
And the fastest way to determine that, says the University of Nebraska–Lincoln’s Chris Bilder, is actually by grouping — not people, but samples.
“The way to think about that is: Imagine if you had a positive rate of 50%,” Bilder said. “If you start putting a lot of samples together, basically every group is going to be positive, and then you lose the benefits of group testing.”
Bilder tells KLIN News he’s been advising the Nebraska Public Health Laboratory on its use of group testing.
He provided an example:
“If a pool of 25 samples were to come back negative, all 25 people could be declared infection-free, reducing the overall number of tests by 24. If that pool instead tested positive, a clinic could separate it into smaller pools — five pools of five samples, for instance — and retest. And if, say, two of those smaller pools turned up positive, the clinic could then individually test the remaining 10 samples. Even in the latter scenario, a clinic would save nine of the original 25 tests.”
The Nebraska Public Health Laboratory currently starts with a pool of five samples, testing each sample only if the pool comes back positive for the novel coronavirus.
Bilder said, “The lab spent 58% fewer tests over its first six days of pooling than it would have by testing just individual samples. That, in turn, meant the lab managed to test 137% more people than it could have using the same resources to test individual samples alone.”
So Bilder and his colleagues, including doctoral candidate Brianna Hitt, have developed a newly released app that does the statistical lifting needed to extract that elusive answer. By plugging in just a few variables — estimated infection rate, a test’s reliability in detecting positive and negative cases, potential pool sizes — users can immediately get that magic number. They also learn the average number of tests they should expect to conduct and, by extension, how many tests they can expect to save.