There are two types of tests available for COVID-19 that can detect whether a person had it in the past (serology testing, which tests for antibodies against SARS-CoV-2, the virus that causes COVID-19), or whether they have it in the present (polymerase chain reaction (PCR) testing, which tests for active infection). This document is designed to explain the differences between PCR and serology testing, and when one test might be used over another.
Why is the test used?
PCR looks for the virus itself in the nose, throat, or other areas in the respiratory tract to determine if there is an active infection with SARS-CoV-2.
Serology looks for antibodies against SARS-CoV-2 in the blood to determine if there has been an infection in the past. Antibodies are formed by the body to fight off infections. IgM is the first antibody that is formed against a germ, so it appears on tests first, usually within 1-2 weeks. The body then forms IgG, which appears on tests about 2 weeks after the illness starts. IgM usually disappears from the blood within a few months, but IgG can last for years. Some antibody tests test for IgM and IgG, and some only test for IgG.
How is the test performed?
In most cases, a nose or throat swab is taken by a healthcare provider, and that swab is sent to the lab for testing.
This test uses a sample of blood.
What does a positive test mean?
A positive PCR test means that the person being tested has an active COVID-19 infection.
A positive antibody test means that the person being tested was infected with COVID-19 in the past and that their immune system developed antibodies to try to fight it off.
When is it helpful?
When is it not as helpful?
Other Information to Help Determine Usefulness of a Test
When new tests come out, they are evaluated for how well they work. You may see the following terms used in reports about new tests.
Sensitivity: Sensitivity is sometimes called the “true positive rate.” It measures how frequently the test is positive when the person being tested actually has the disease. For example, when a test has 80% sensitivity, the test detects 80% of patients with the disease (true positives). However, 20% of patients with the disease are not detected (false negatives) by the test.
Specificity: Specificity is sometimes called the “true negative rate.” It measures how frequently the test is negative when the person being tested doesn’t have the disease. For example, when a test has 80% specificity, the test correctly reports 80% of patients without the disease as test negative (true negatives). However, 20% of patients without the disease are incorrectly identified as testing positive (false positives) by the test.
Positive Predictive Value: Positive predictive value is a measure of how likely it is that a positive test is a true positive rather than a false positive. This is dependent on how many people in the population being tested have had the disease. When there are very few people in the population that have had the disease, then there is a higher chance that a positive test is a false positive. When there are many people in a population that have had the disease, then there is a higher chance that a positive test is a true positive.
Source: TEXAS Health and Human Services