Nasal Swab Image
Applying the NIST team's findings could make nasal swab tests up to 10 times more sensitive, improving our ability to identify people who are infected but not displaying COVID-19 symptoms. (Credit: Shutterstock)

A research team at the National Institute of Standards and Technology (NIST) has developed a way to increase the sensitivity of the primary test used to detect the SARS-CoV-2 virus, which causes COVID-19. Applying their findings to computerized test equipment could improve the ability to identify people who are infected but do not exhibit symptoms.

The team’s results describe a mathematical technique for perceiving comparatively faint signals in diagnostic test data that indicate the presence of the virus. These signals can escape detection when the number of viral particles found in a patient’s nasal swab test sample is low. The team’s method helps a modest signal stand out more clearly.

The researchers’ findings prove that the data from a positive test, when expressed in graphical form, takes on a recognizable shape that is always the same. While it was known previously that the shape’s position could vary, the team learned that its size can vary as well. Reprogramming test equipment to recognize this shape, regardless of size or location, is the key to improving test sensitivity.

While the swab test method usually works well in practice, it can lack sensitivity to low viral particle counts. The test starts with the genetic material that is present and doubles it, then doubles it again, up to 40 times over, so that the fluorescent markers generate enough light to trigger a detector. However, when the initial viral count is low, there may be false starts in the first few cycles. In these cases, even 40 doublings may not build a spike tall enough — or a fluorescence bright enough — to rise above the detection threshold. This issue can cause problems like inconclusive tests or “false negatives,” meaning a person carries the virus but the test does not reveal it.

The NIST researchers found that the shape of a positive test graph — a flat, noisy beginning followed by a spike — is found even in data that currently does not trigger a positive test result. Incorporating their findings into tests would immediately help the pandemic response because it would help determine the number of asymptomatic and presymptomatic cases more accurately.