A project of     Die Junge Akademie     with members of     TU Braunschweig University of Stuttgart Arctoris

Online Tool for Scenario-Based Mass Testing for COVID-19

This online tool enables you to model the performance of various pool-based testing strategies for a specific area in response to the COVID-19 pandemic. It was developed based on the reference given on the background page, available as a preprint at https://arxiv.org/abs/2004.11851.

Mass testing is increasingly understood by scientists and politicians as the only viable exit strategy from COVID-19 lockdown (see our short collection of statements). However, currently every person gets tested individually - an approach that requires too much time and resources. In light of limited testing capacities, sample pooling (also known as group testing) is a highly practicable solution to increase testing throughput considerably. To that end, several samples are combined in one test, and further testing is undertaken only in case of a positive result. We present the results of the first extensive study comparing the most relevant methods for sample pooling published so far. We determine the efficiency of the different methods using a simulation approach, quantifying how many infected cases we can identify per test. Assuming an infection rate of 1%, the conventional approach (individual testing) will require an average of 100 tests to identify one infected case (i.e. 0.01 cases are detected per test). However, when pooling e.g. ten samples, there is a higher likelihood of identifying groups of ten as non-infected with a single test, thereby significantly reducing the total number of tests required. Our results show that for current infection rates, advanced sample pooling methods can identify up to ten times more cases per test than individual testing.

Disclaimer: Please note that these findings are generated using non-audited, research-grade open source software. The code can be found here: https://github.com/SC-SGS/covid19-pooling.

Instructions: Please adapt the input parameters to reflect your specific situation - you will then receive a graphical representation showing the performance of different testing strategies for infection rates ranging from 0.1% to 30%.

Input Parameters: Test Characteristics

Input Parameters: Population Characteristics

Alternatively, use the following shortcuts to import the parameters from the manuscript:

Please note: The results obtained via the online tool differ from those described in the paper because of limitations in available computing power for online simulations. Therefore the reference population and number of repetitions have been reduced to reduce calculation time. Furthermore the optimal group sizes are determined based on precalculated values and not on individual simulations. For full-scale simulations, please use the original code available on GitHub.

Computations can take up to one minute due to the simulations running in the background - results will be displayed once complete.

Instructions: Hovering over a data point shows simulated results. Draw rectangle to zoom in. Scenarios and standard deviation bars can be switched on and off in the legend.

Method Time to test 10% of the population
Individual testing 234.2 days
2-level pooling 46.0 days
Binary splitting 27.9 days
Recursive binary splitting 24.7 days
Purim 31.8 days
Sobel-R1 20.8 days

Parameters: population: 328,240,000; testing capacity p.d.: 146,000; infection rate: 1.0%