Biology Magazine

How to Make a Fast but Bad COVID-19 Test Good

Posted on the 28 July 2020 by Ccc1685 @ccc1685

Among the myriad of problems we are having with the COVID-19 pandemic, faster testing is one we could actually improve. The standard test for the presence of SARS-CoV-2 virus uses PCR (polymerase chain reaction), which amplifies targeted viral RNA. It is accurate (high specificity) but requires relatively expensive equipment and reagents that are currently in short supply. There are reports of wait times of over a week, which renders a test useless for contact tracing.

An alternative to PCR is an antigen test that tests for the presence of protein fragments associated with COVID-19. These tests can in principle be very cheap and fast, and could even be administered on paper strips. They are generally much more unreliable than PCR and thus have not been widely adopted. However, as I show below by applying the test multiple times, the noise can be suppressed and a poor test can be made arbitrarily good.

The performance of binary tests are usually gauged by two quantities - sensitivity and specificity. Sensitivity is the probability that you test positive (i.e are infected) given that you actually are positive (true positive rate). Specificity is the probability that you test negative if you actually are negative (true negative rate). For a pandemic, sensitivity is more important than specificity because missing someone who is infected means you could put lots of people at risk while a false positive just means the person falsely testing positive is inconvenienced (provided they cooperatively self-isolate). Current PCR tests have very high specificity but relatively low sensitivity (as low as 0.7) and since we don't have enough capability to retest, a lot of tested infected people could be escaping detection.

The way to make any test have arbitrarily high sensitivity and specificity is to apply it multiple times and take some sort of average. However, you want to do this with the fewest number of applications. Suppose we administer How to make a fast but bad COVID-19 test good tests on the same subject, the probability of getting more than How to make a fast but bad COVID-19 test good positive tests if the person is positive is How to make a fast but bad COVID-19 test good, where How to make a fast but bad COVID-19 test good is the cumulative distribution function of the Binomial distribution (i.e. probability that the number of Binomial distributed events is less than or equal to How to make a fast but bad COVID-19 test good). If the person is negative then the probability of How to make a fast but bad COVID-19 test good or fewer positives is How to make a fast but bad COVID-19 test good. We thus want to find the minimal How to make a fast but bad COVID-19 test good given a desired sensitivity and specificity, How to make a fast but bad COVID-19 test good and How to make a fast but bad COVID-19 test good. This means that we need to solve the constrained optimization problem: find the minimal How to make a fast but bad COVID-19 test good under the constraint that How to make a fast but bad COVID-19 test good, How to make a fast but bad COVID-19 test good and How to make a fast but bad COVID-19 test good. How to make a fast but bad COVID-19 test good decreases and How to make a fast but bad COVID-19 test good increases with increasing How to make a fast but bad COVID-19 test good and vice versa for How to make a fast but bad COVID-19 test good. We can easily solve this problem by sequentially increasing How to make a fast but bad COVID-19 test good and scanning through How to make a fast but bad COVID-19 test good until the two constraints are met. I've included the Julia code to do this below. For example, starting with a test with sensitivity .7 and specificity 1 (like a PCR test), you can create a new test with greater than .95 sensitivity and specificity, by administering the test 3 times and looking for a single positive test. However, if the specificity drops to .7 then you would need to find more than 8 positives out of 17 applications to be 95% sure you have COVID-19.


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