A preliminary study, posted online this week by researchers at the Australian National University and elsewhere, estimates 71,000 Australians had COVID-19 by mid-July — 60,000 more than official number of cases diagnosed by that stage.
The study involved testing 2,991 elective surgery patients in ten hospitals across four states, to see whether they had antibodies against SARS-CoV-2, the virus that causes COVID-19.
The study initially found 41 positive patients (1.4%), but then adjusted for the false positives that would arise due to the imperfect specificity of the antibody test, which the researchers estimate would produce 11 false positives for every 1,000 tests. This yielded an estimated prevalence of 0.28% — or eight “true” positives from the 2,991 people sampled.
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The researchers then extrapolated this estimate, including its uncertainty parameters, to the Australian population as a whole. They ultimately concluded the number of Australians with SARS-CoV-2 antibodies — and who have therefore presumably been infected with COVID-19 — is somewhere between zero and 181,050, and most likely about 71,000.
This begs two main questions: should this alter our view on how best to contain the spread of COVID-19, and are there any limitations to the study that we should be aware of?
Let’s begin with the latter question. Here are four key things to consider when interpreting the results.
1. False positives.
In countries with very low COVID-19 rates, such as Australia, the key requirement of an antibody test is to be highly specific — that is, to avoid false positives. This is even more important than being highly sensitive (avoiding false negatives).
The antibody test used in the new study reportedly has a specificity of 98.9%, and a sensitivity of 100%. This means, for every 1,000 tests, we can expect 11 false positives and no false negatives.
Imagine a place with high prevalence of the virus, such as New York City, where roughly 20% of people are estimated to have had COVID-19. A sample of 1,000 would, on average, contain 200 COVID-19 positive people, of whom the test would correctly identify all 200, with no false negatives. It would also find 11 people positive who were actually negative, giving an estimated prevalence of 211 out of 1,000, or 21.1% — which is close to the true figure.