Universal COVID testing never works

During the COVID pandemic, universal testing had once been a topic in Hong Kong. Even if we can handle millions of testings a day, is it a good approach?

Let’s assume 99.9% accurate for positive and 99% for negative and 80,000 people are infected.

We will find 7,920,000 x 0.001 + 80,000 x 0.99 = 87,120 people infected. But among them, only 1 - (80,000 x 0.99/87,120) = 9.1% are not infected. They are false positive cases. While 1%, 800 infected people will test negative! Given that the figures I used were almost ideal!

As a result, universal testing is pointless. It will find many false positive cases, while hundreds false negative cases will be missed.

So what are the right moves? Here are my suggestions.

1. We test all patients who thought they have got the virus, even with very mild symptoms. So we can detect the newly infected cases and isolate them as soon as possible.
2. We test the correlations of the confirm cases sooner the better, obviously they are more likely to be infected than any others.
3. We keep testing high risk population. Such as staff in hospitals, nurses, doctors, etc. If they have got the virus, they will infect the most vulnerable people.
4. Start random testings. Let’s test 500 people randomly, from all walks of life. It will give us a basic idea how the virus spreads in the community. We can do this on monthly basis.