CSH Policy Brief 4/2020

Pooling corona tests could boost test efficiency by a factor of 10

According to calculations by the Complexity Science Hub Vienna (CSH), significantly more people could be tested for SARS-CoV-19 with the tests currently available if several samples were combined into one test [1]. The method presented here indicates the optimal pooling size. With 10,000 actually infected persons in Austria, about 45,000 people could be tested with 3,000 tests available daily. If the number of infected persons is 100,000, about 15,000 people could be tested daily. Pooling could thus help to significantly alleviate bottlenecks in testing.

Background

Many countries, including Austria, face a shortage of tests for the SARS-CoV-2 virus. Pooling strategies for testing potentially infected persons are a practically free way of multiplying the efficiency of the tests while the level of infection of the population is still low.

In the simplest version of pooling, samples from several people are given together and tested with a single test. If the test is negative, all the people tested are negative. If the test is positive, all persons are tested individually. If the infection level of the population is low, this can lead to considerable increases in testing efficiency.

The method

In pooling strategies, samples from several people are combined and evaluated in one test. In this way, the effective number of people measured per test can be increased massively. The quality of the method depends on the number of infections in the population. With an infection rate of 0.1 percent, up to 15 persons can be measured per test, i.e. the same number of tests can test 15 times more persons. At an infection rate of 1 percent, 5 people can be tested per test. With 10 percent infected, the effectiveness of the method drops to under 2 persons per test.

Results in detail

The proposed method is a formula which, on the one hand, indicates how many people can be pooled, i.e. how many samples are to be measured together in one test. On the other hand, it estimates the degree of efficiency: i.e. how many people can be effectively tested with one test.

The results are shown in Figure 1 (blue curve). The x-axis shows the infection level of the population, the y-axis the optimal pooling size (see Figure 1 [a]).

Figure 1 (b) shows the number of people that can be measured with one test.

Figure 1 (c) shows the expected error rate (“false negatives”) of the pooling method.

Pooling of SARS-CoV-2 samples