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Like so many clever puzzle answers, this doesn't take into account the effect of measurement error. The tests apparently have a false negative rate of 20% as i

Like so many clever puzzle answers, this doesn't take into account the effect of measurement error. The tests apparently have a false negative rate of 20% as it is. Now start mixing samples together, causing more dilution of the positive samples, and the false negative rate is bound to go up further. Now you have to do studies to figure out what the false negative rate is going to be, and put that into your model as well. This could get impractical fast.

jupp0r 6y ago
I’m not a virologist, but from my understanding the false negative rate is caused when collecting swab samples and not related to the concentration during the PCR. There are also thresholds in the process (mainly the number of reaction cycles) that can be tuned to account for lower concentrations.
- virusduck 6y ago
  That is correct.
mistrial9 6y ago
> tests apparently have a false negative rate of 20% no, there are different kinds of tests, and from different manufacturers, and the materials are handled differently.. Please do not spread this information as 'facts'
acqq 6y ago
Let's imagine, for the sake of argument, that the tests are indeed with the false negative rate of 20%. Imagine also that the tests are done to protect a community of 100000 people and that there is simply no more than 10000 test available during some period, and an infrastructure that can never do more than e.g. 1000 tests per day, which all could be valid assumption in real life: the number of tests is limited, the infrastructure is limited etc, that's why pooling is discussed anyway. The first question is "does it make more sense to test everybody once or those regularly exposed and capable to pass to many others more than once." I believe that the answer to the first question is "test those regularly exposed more than once" (e.g. in the hospitals, it would probably be preferable to test hospital workers regularly if you don't want them to pass the infection to the patients who aren't infected). The second question is only then "should I pool the tests and perform the tests more often or not pool the tests and perform them infrequently". Again, I believe the answer is "better testing more often" if the tests are done on those without any symptoms. Because the if goal is to catch those who could transmit without them knowing they are infected, and they are regularly potentially exposed (e.g. workers in hospitals), even if the single test makes a false negative 1 of 5 times, having them 10 times more often significantly increases the chances to catch all those who get infected early enough to minimize their chance to infect a lot of people. The practice is of course not easy: one has to be able to not depend on e.g. 10 workers at once when only one is potentially infected, until "which one it is" is resolved. In short, the pooling could work better if the actual prevalence is low, as the papers also recognize. I guess the main idea is: the reasonable goals for all testings aren't the same, and the logic of "what has sense" for what goal should be adapted correspondingly, and no approach should be immediately declared wrong without carefully considering what is the real goal of the tests. Another scenario where one wants to use tests is to decide when the already sick people aren't any more capable infecting somebody. There false negative rate is also worrying. Again, what is the goal can change depending on how empty the hospital beds are -- in situations like it was in New York there weren't enough places in hospitals, and "making place for new patients" was priority, which, it was admitted, allowed more people getting infected -- still sick people were dismissed, only to infect others.
enchiridion 6y ago
Didn't realize you're on HN. Your channel is great! Things like the pantograph make me think about computation as a physical process.
zmmmmm 6y ago
> this doesn't take into account the effect of measurement error. So then how are you otherwise interpreting the paragraph discussing robustness to measurement error? If I understand it correctly, it is one of the major benefits of the proposed method.

jupp0r 6y ago

I’m not a virologist, but from my understanding the false negative rate is caused when collecting swab samples and not related to the concentration during the PCR. There are also thresholds in the process (mainly the number of reaction cycles) that can be tuned to account for lower concentrations.

virusduck 6y ago
That is correct.

mistrial9 6y ago

> tests apparently have a false negative rate of 20% no, there are different kinds of tests, and from different manufacturers, and the materials are handled differently.. Please do not spread this information as 'facts'

acqq 6y ago

Let's imagine, for the sake of argument, that the tests are indeed with the false negative rate of 20%. Imagine also that the tests are done to protect a community of 100000 people and that there is simply no more than 10000 test available during some period, and an infrastructure that can never do more than e.g. 1000 tests per day, which all could be valid assumption in real life: the number of tests is limited, the infrastructure is limited etc, that's why pooling is discussed anyway. The first question is "does it make more sense to test everybody once or those regularly exposed and capable to pass to many others more than once." I believe that the answer to the first question is "test those regularly exposed more than once" (e.g. in the hospitals, it would probably be preferable to test hospital workers regularly if you don't want them to pass the infection to the patients who aren't infected). The second question is only then "should I pool the tests and perform the tests more often or not pool the tests and perform them infrequently". Again, I believe the answer is "better testing more often" if the tests are done on those without any symptoms. Because the if goal is to catch those who could transmit without them knowing they are infected, and they are regularly potentially exposed (e.g. workers in hospitals), even if the single test makes a false negative 1 of 5 times, having them 10 times more often significantly increases the chances to catch all those who get infected early enough to minimize their chance to infect a lot of people. The practice is of course not easy: one has to be able to not depend on e.g. 10 workers at once when only one is potentially infected, until "which one it is" is resolved. In short, the pooling could work better if the actual prevalence is low, as the papers also recognize. I guess the main idea is: the reasonable goals for all testings aren't the same, and the logic of "what has sense" for what goal should be adapted correspondingly, and no approach should be immediately declared wrong without carefully considering what is the real goal of the tests. Another scenario where one wants to use tests is to decide when the already sick people aren't any more capable infecting somebody. There false negative rate is also worrying. Again, what is the goal can change depending on how empty the hospital beds are -- in situations like it was in New York there weren't enough places in hospitals, and "making place for new patients" was priority, which, it was admitted, allowed more people getting infected -- still sick people were dismissed, only to infect others.

enchiridion 6y ago

Didn't realize you're on HN. Your channel is great! Things like the pantograph make me think about computation as a physical process.

zmmmmm 6y ago

> this doesn't take into account the effect of measurement error. So then how are you otherwise interpreting the paragraph discussing robustness to measurement error? If I understand it correctly, it is one of the major benefits of the proposed method.