Interpreting Positive COVID-19 Antibody Tests

Note: this tool does not constitute medical advice. Please consult a physician before making medical decisions related to tests for the COVID-19 virus or virus antibodies. Positive antibody test results may be incorrect, and anitbody positive status is not equivalent to immunity.

A number of large-scale studies are underway to understand the prevalence of COVID-19 antibodies in various populations. As with any medical test, antibody testing is not infallible. Some people without the antibody will test positive (i.e. a false positive) and some with the antibody will test negative (i.e. a false negative).

This tool can be used to interpret the meaning of an individual positive test result in the context of an antibody population study. Proper interpretation depends not only on the accuracy of a test, but also on the prevalence of COVID-19 antibodies in the population being tested. You'll notice that whenever the false positive rate exceeds the prevalence, most positive test results will be incorrect. This situation is known as a false positive paradox.

Note that this tool does not factor in whether an individual has previously tested positive for the COVID-19 virus or displayed symptoms of COVID-19 - such factors will make a positive antibody test result more likely to be correct. The tool is meant for interpreting positive results from population studies where participants are no more or less likely to have had the COVID-19 virus than their general community at large.

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The visualization below represents a population being tested for the COVID-19 virus antibody. Each square represents 0.1% of the population being tested and is categorized as one of the following test outcomes.

Use the sliders below to explore the effects of test accuracy and antibody prevalence on the Positive Predictive Value of a test—the likelihood that a positive test result is correct.