Mathematical Decision Support How Covid-19 Testing Can Become More Efficient
With the help of so-called pooling procedures, samples from different people can be combined into a pool and tested for Covid-19 collectively in a single test kit. An interdisciplinary team of mathematicians, computer scientists and medical doctors from the Junge Akademie, the Technische Universität Braunschweig, the Universität Stuttgart and the company Arctoris has developed a decision support tool that calculates which method is most effective in identifying all Covid-19 patients in a positive sample pool. Their simulations show that in Germany, pool-based test methods can be about eight times more efficient than individual tests. The team recently published the results in a preprint on “arXiv” and as a website.
In sample pooling, the sample material of different people is combined into one sample (pool) and tested together. This can save time and test capacities when the infection rate is low compared to individual testing. If the sample is negative, none of the individual samples contained in the pool need to be tested separately. If the result is positive, further tests are performed. Depending on the scenario, different procedures are suitable for this.
Mathematical decision support
The researchers simulated a total of five different pool testing procedures and individual testing using a mathematical modelling approach for five countries. They took into account parameters such as infection rate, test characteristics, population size and test capacities. Based on these parameters, algorithms calculate which of the pooling methods is most effective in each case.
The comparative analysis is intended to serve as a decision-making aid for laboratories and political decision-makers. In addition, the team has developed an interactive website on which scenarios for other countries or cities can also be modelled.
“What is new about our approach is the comparison between the various existing test strategies and the recommendation of which procedure would be most suitable in a given situation in order to identify a maximum number of sick people in the shortest possible time with limited resources,” says Professor Timo de Wolff from the Technische Universität Braunschweig and member of the Junge Akademie, who initiated the project. The pool testing approach has been used in medicine for several decades, for example for testing blood donations for viruses. Recently, it was shown in the laboratory that such test methods are also possible for Covid-19. With regard to Covid-19 testing, this approach is currently being taken up in a number of preprints.
About eight times more efficient
In the preprint now published, the researchers show that pool-based testing procedures can be about eight times more efficient than individual tests in current scenarios at an infection rate of 1 percent. At such a low infection rate, one tenth of the German population could be tested for Covid-19 within approximately 10 days using realistic and optimised pool testing strategies. Pool-based testing strategies can also greatly reduce the number of false-positive diagnoses.
With the help of such approaches, broad testing within a population would be possible, in particular to quickly identify symptom-free patients. However, their effectiveness decreases with increasing infection rates, which is why their application is particularly useful in cases of low infection rates.
About the project
In their publication, the researchers have simulated situations in the USA, Germany, Great Britain, Italy and Singapore to reflect a wide variety of population sizes and test capacities. The project involves Professor Timo de Wolff and Janin Heuer, TU Braunschweig, Professor Dirk Pflüger and Michael Rehme, Universität Stuttgart, and Dr. Dr. Martin-Immanuel Bittner, Arctoris, Oxford. Timo de Wolff, Dirk Pflüger and Martin-Immanuel Bittner are members of the Junge Akademie. The preprint and the code are publicly accessible. The interactive website can be found at https://ipvs.informatik.uni-stuttgart.de/sgs/cgi-bin/JA/covid19/.
The project was financed by the Junge Akademie. The publication has so far appeared as a preprint on “arXiv” and is available at https://arxiv.org/abs/2004.11851.
De Wolff, Timo; Pflüger, Dirk; Rehme, Michael; Heuer, Janin; Bittner, Martin-Immanuel: Evaluation of Pool-based Testing Approaches to Enable Population-wide Screening for COVID-19 (arXiv:2004.11851)
Joint press release of the TU Braunschweig, the Universität Stuttgart, the company Arctoris and the Junge Akademie.