Pooling samples could boost corona test efficiency


Mar 22, 2020

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

Fig. 1. (a) Optimal pooling size for a given infection level in the population. (b) Individuals that can be effectively measured by a test. The tables within the graphs show the situation for low levels of infection. (c) Error rates ("false negatives") of the pooling procedure.
© CSH Vienna

Fig. 1 (a) Optimal pooling size for a given infection level in the population.
(b) Individuals that can be effectively measured by a test. The tables within the graphs show the situation for low levels of infection.
(c) Error rates (“false negatives”) of the pooling procedure.

Conclusion of the CSH

 

Assuming that there are currently 10,000 actually infected persons in Austria, the optimal pooling size is approximately 32 samples per test. In this case, with 3,000 tests available daily, about 45,000 people could be tested in Austria.

 

If the number of infected persons is 100,000, the optimal pooling size is 11, which means that with 3,000 tests, 15,000 persons could be tested daily.

 

Assuming a number between 10,000 and 100,000 infected persons, the optimal pooling size will be about 20 samples per test. Here, it can be assumed that about 30,000 people can be tested daily.

 

As the infection level of the population increases, the pooling size as shown in Figure 1 (a) should be reduced.

 

Of course, there could be practical and logistical problems in laboratories and testing facilities that would make the method hard to implement.

 

 

CSH scientists Rudolf Hanel and Stefan Thurner, Medical University of Vienna

 

 

[1]   R Hanel, S Thurner, Boosting test efficiency by pooled testing strategies for SARS-CoV-2, March 21, 2020

 

 

 

CSH Policy Brief 4/2020

 

Pooling von Coronavirus-Tests kann die Anzahl der getesteten Personen pro verfügbarem Test massiv erhöhen

 

Laut Berechnungen des Complexity Science Hub Vienna (CSH) könnten mit den derzeit verfügbaren Tests deutlich mehr Personen auf SARS-CoV-19 untersucht werden, wenn mehrere Proben zu einem Test zusammengeführt werden [1]. Die hier vorgestellte Methode gibt die optimale Pooling-Größe an. Bei 10.000 tatsächlich infizierten Personen in Österreich könnten mit 3.000 täglich verfügbaren Tests etwa 45.000 Menschen getestet werden. Liegt die Zahl der Infizierten bei 100.000, könnten etwa 15.000 Menschen täglich getestet werden. Pooling könnte somit dabei helfen, Engpässe bei den Tests deutlich zu entschärfen.

 

Hintergrund

 

Viele Länder, darunter auch Österreich, sind mit einer Knappheit von Tests für das SARS-CoV-2-Virus konfrontiert. Pooling-Strategien zum Testen von möglicherweise Infizierten sind eine praktisch kostenlose Möglichkeit, die Effizienz der Tests zu vervielfachen, solange der Infektionsgrad der Bevölkerung noch gering ist.

 

In der einfachsten Version von Pooling werden Proben von mehreren Personen zusammen gegeben und mit einem einzigen Test getestet. Ist der Test negativ, sind alle gemessenen Personen negativ. Ist der Test positiv, werden alle Personen einzeln getestet. Bei einem geringen Infektionsgrad der Bevölkerung kann man so beträchtliche Effizienzsteigerungen beim Testen erreichen.

 

Das Verfahren

 

Bei Pooling-Strategien werden Proben von mehreren Personen zusammengefasst und in einem Test ausgewertet. So kann die effektive Zahl der gemessenen Personen pro Test massiv gesteigert werden. Die Qualität der Methode hängt von der Zahl der Infektionen in der Population ab. Bei einem Infektionslevel von 0,1 Prozent kann die Methode bis zu 15 Personen pro Test messen, das heißt, mit derselben Anzahl von Tests können 15 Mal mehr Personen getestet werden. Bei einem Infektionsgrad von 1 Prozent können noch immer 5 Personen pro Test getestet werden. Bei 10 Prozent Infizierten sinkt die Wirksamkeit der Methode auf knapp 2 Personen pro Test.

 

Ergebnisse im Detail

 

Die vorgeschlagene Methode ist eine Formel, die einerseits angibt, wie viele Personen gepoolt werden, also wie viele Proben zusammen in einem Test gemessen werden sollen. Andererseits schätzt sie den Effizienzgrad ab: also wie viele Leute effektiv mit einem Test getestet werden können.

 

Die Ergebnisse sind in Abb. 1 dargestellt (blaue Kurve). Die x-Achse zeigt den Infektionsgrad der Bevölkerung, die y-Achse die optimale Pooling-Größe (Abb. 1 (a)).

 

Abb. 1 (b) zeigt die Anzahl der Personen, die mit einem Test gemessen werden können.

 

Abb. 1 (c) gibt die zu erwartende Fehlerrate („false negatives“) der Pooling-Methode an.

 

 

 Pooling von SARS-CoV-2-Samples

Fig. 1. (a) Optimal pooling size for a given infection level in the population. (b) Individuals that can be effectively measured by a test. The tables within the graphs show the situation for low levels of infection. (c) Error rates ("false negatives") of the pooling procedure.
© CSH Vienna

Abb 1 (a) Optimale Pooling-Größe für einen gegebenen Infektionsgrad in der Bevölkerung.
(b) Personen, die effektiv mit einem Test gemessen werden können. Die Vergrößerungen innerhalb der Grafik zeigen die Situation für niedrige Infektionsgrade.
(c) Fehlerraten („false negatives“) des Pooling-Verfahrens.

Fazit des CSH

 

Unter der Annahme, dass es derzeit in Österreich 10.000 tatsächlich infizierte Personen gibt, ergibt sich eine optimale Pooling-Größe von ca. 32 Samples pro Test. In Österreich könnten in diesem Fall mit 3.000 täglich verfügbaren Tests etwa 45.000 Menschen getestet werden.

 

Liegt die Zahl der Infizierten bei 100.000 Personen, ist die optimale Pooling-Größe 11. Damit könnten mit 3.000 Tests 15.000 Personen täglich getestet werden.

 

Nimmt man eine Zahl zwischen 10.000 und 100.000 Infizierten an, wird die optimale Pooling-Größe bei etwa 20 Samples pro Test liegen. Hier kann man davon ausgehen, etwa 30.000 Menschen täglich testen zu können.

 

Mit der Erhöhung des Infektionsgrades der Bevölkerung ist die Pooling-Größe gemäß Abb. 1 (a) zu reduzieren.

 

Einschränkend ist zu sagen, dass diesem Vorschlag konkrete Probleme in den Laboren und Teststellen entgegenstehen könnten.

 

 

 

CSH Wissenschaftler Rudolf Hanel und Stefan Thurner, MedUni Wien

 

 

[1]   R Hanel, S Thurner, Boosting test efficiency by pooled testing strategies for SARS-CoV-2, March 21, 2020

About the CSH

The Complexity Science Hub Vienna was founded with the aim of using Big Data for the benefit of society. Among other things, the CSH systematically and strategically prepares large data sets so that they can be used in agent-based models. These simulations allow the effects of decisions in complex situations to be tested in advance and systematically assessed. Thus, the CSH provides fact-based foundations for an evidence-based governance.

CSH Policy Briefs  present socially relevant statements that can be derived from CSH research results.

Über den CSH

Der Complexity Science Hub Vienna wurde gegründet mit dem Ziel, Big Data zum Wohle der Gesellschaft zu nutzen. Unter anderem werden am CSH große Datensätze systematisch und strategisch so aufbereitet, dass sie in Agenten-basierten Modellen verwendet werden können. Diese Simulationen erlauben es, Auswirkungen von Entscheidungen in komplexen Situationen vorab zu testen und systematisch einzuschätzen. Damit liefert der CSH faktenbasierte Grundlagen für eine evidenzbasierte Governance.

CSH Policy Briefs enthalten gesellschaftlich relevante Aussagen, die sich aus Forschungsergebnissen des CSH ableiten lassen.


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