Variable pool testing for infection spread estimation
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We present a method for efficient estimation of the prevalence of infection in a population with high accuracy using only a small number of tests. The presented approach uses pool testing with a mix of pool sizes of various sizes. The test results are then combined to generate an accurate estimation over a wide range of infection probabilities. This method does not require an initial guess on the infection probability. We show that, using the suggested method, even a set of only 50 tests with a total of only 1000 samples can produce reasonable estimation over a wide range of probabilities. A measurement set with only 100 tests is shown to achieve 25% accuracy over infection probabilities from 0.001 to 0.5. The presented method is applicable to COVID-19 testing.
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