COVID rapid antigen tests, by the numbers

Worth the Test · Written by an emergency physician · Last reviewed September 2026

For many people, the COVID-19 rapid antigen test is one of the few diagnostic tests they have ever run, read, and acted on entirely by themselves. That makes it the best teaching example in this library: everyone has stared at that little window wondering what one faint line — or its absence — actually proves. The honest answer depends on three numbers, and one of them is about you, not the test.

The accuracy numbers, and the twist

The main accuracy evidence comes from a Cochrane systematic review by Dinnes and colleagues, whose 2022 version pooled 152 evaluations across more than 100,000 samples. Its headline figures:

• Sensitivity ≈ 73% in people with symptoms — during symptomatic infection, roughly 27 in 100 infected people still test negative on a single rapid test.
• Sensitivity ≈ 55% in people without symptoms — used for asymptomatic screening, the same kind of test misses almost half of infections.
• Specificity ≈ 99% or better in both groups (99.1% symptomatic, 99.7% asymptomatic) — false positives are genuinely rare.

The twist is that first pair. "How accurate is a rapid test?" has no single answer, because sensitivity is not a fixed property of the plastic cassette — it depends on who is being tested. Symptomatic people tend to be tested at peak viral load, when antigen is abundant; asymptomatic people include many caught early or late in infection, with less antigen to find. This is spectrum effect: the same test, measured in two populations, earns two different report cards. It is the sharpest everyday example of a caveat that applies to every test on this site — a published sensitivity travels with the population it was measured in. (The sensitivity-and-specificity guide unpacks this.)

The review's finer cuts point the same way. Among people with symptoms, average sensitivity was 80.9% (95% CI 76.9% to 84.4%) in the first week after symptoms began, but 53.8% in the second week. Sensitivity fell steadily as the amount of virus in the sample fell. And it varied between brands: used according to the manufacturer's instructions, individual brands averaged anywhere from 34.3% to 91.3% sensitivity in people with symptoms. "A rapid test" is really a family of tests, each read at a particular moment in an infection.

Worked example 1 — symptomatic, during a wave

Say you have a sore throat and fever during a winter wave, when a substantial share of similar symptomatic people — call it 20%, an illustrative figure, not a measured one — actually have COVID. Test 1,000 such people with a 73% / 99.1% test. First the truth: 200 infected, 800 not.

Of the 200 infected, 200 × 0.73 = 146 test positive; 54 test negative anyway. Of the 800 not infected, 800 × 0.009 ≈ 7 false positives; 793 correctly negative.

Two conclusions fall out, pointing in opposite directions:

A positive is near-decisive: PPV = 146 ÷ 153 ≈ 95%. With symptoms, in a wave, a line on the test settles it for practical purposes.

A negative settles much less: 54 of the 847 negatives — about 1 in 16 — are actually infected. Starting from 20%, one negative test only drops you to about 6%.

That residual 6% is why a single negative rapid test, taken while you're actively symptomatic, was never treated as an all-clear. The test's LR− is (1 − 0.73) ÷ 0.991 ≈ 0.27 — a real push, but nowhere near the below-0.1 territory that confidently rules things out. The standard remedies are the two classic moves of pre-test/post-test reasoning: apply a stronger test (a lab NAAT — the Cochrane authors frame antigen tests as triage for exactly this reason), or apply the same weak test again after the odds have had time to shift — antigen rises as an infection develops, so tomorrow's test is not just a coin re-flip.

Worked example 2 — no symptoms, quiet week

Now the same physical act — swab, swirl, wait — before visiting a relative, with no symptoms and no known exposure, in a quiet week where perhaps 1 in 200 similar people (0.5%, again illustrative) is infectious. The applicable numbers are now 55% sensitivity and 99.7% specificity, and the base rate has collapsed by a factor of forty. Run 1,000 such people: 5 infected, 995 not. The test catches 5 × 0.547 ≈ 3 of the infected and falsely flags 995 × 0.003 ≈ 3 of the healthy.

PPV = (0.547 × 0.005) ÷ [(0.547 × 0.005) + (0.003 × 0.995)] = 0.00274 ÷ 0.00572 ≈ 0.48 — a positive is a coin flip.

Sit with that: a test with 99.7% specificity — a false-positive rate of three per thousand — still produces positives that are only about half real, because at a 0.5% base rate the healthy crowd outnumbers the infected two hundred to one. This is the base-rate fallacy in its purest consumer form, and it is the same structure as the HIV screening paradox, where an even better test (LR+ ≈ 200) yields positives that are barely better than even odds at population prevalence. The rapid test's LR+ here is 0.547 ÷ 0.003 ≈ 180 — comparable pull — and the base rate eats it just the same.

That answer is also fragile. A 2025 update of the Cochrane review, limited to people without symptoms, put average specificity at 99.5% rather than 99.7% (and sensitivity at 55.0%). At that figure the same 1,000 people produce about 5 false positives instead of 3, and the PPV falls to roughly 36% — nearer one in three than a coin flip. At a low base rate, two-tenths of a percentage point of specificity moves the answer a long way.

The negative column, meanwhile, is quietly excellent in this scenario — residual risk about 0.2%, not because the test ruled infection out (at 55% sensitivity it barely argued) but because there was almost nothing to rule out. When the base rate is low enough, a negative result is mostly confirming what was already true.

One test, two verdicts

Put the two worked examples side by side and the deepest lesson on this site falls out: the same cassette, the same chemistry, the same faint pink line meant "you almost certainly have COVID" in one situation and "even odds" in the other. Nothing about the test changed. What changed was the pre-test probability — symptoms, exposure, and how much virus was circulating that week. A test result is not a verdict; it is an update to whatever the odds were before you swabbed. That is the entire content of Bayes' theorem, and the rapid test put a worked example of it in everyone's bathroom cabinet.

It also explains the era's practical rituals. Repeating a negative test a day or two later, confirming a surprising positive with a lab test, trusting a positive more in January than in June — each is an informal Bayesian move: stack a second likelihood ratio, or re-read the same result against a different base rate. The first of those is now official advice: the U.S. Food and Drug Administration recommends testing again 48 hours after a negative at-home antigen result — at least two tests in all for people with symptoms, at least three for people without — to reduce the risk that an infection is missed, and in November 2022 it required manufacturers to put that advice on the tests' labeling. The serial-testing guide works through what repetition does to both error rates, in both directions.