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Open AccessArticle

Group Testing-Based Robust Algorithm for Diagnosis of COVID-19

Department of Convergence Software, Mokpo National University, Muan 58554, Korea
Diagnostics 2020, 10(6), 396; https://doi.org/10.3390/diagnostics10060396
Received: 28 April 2020 / Revised: 1 June 2020 / Accepted: 8 June 2020 / Published: 11 June 2020
At the time of writing, the COVID-19 infection is spreading rapidly. Currently, there is no vaccine or treatment, and researchers around the world are attempting to fight the infection. In this paper, we consider a diagnosis method for COVID-19, which is characterized by a very rapid rate of infection and is widespread. A possible method for avoiding severe infections is to stop the spread of the infection in advance by the prompt and accurate diagnosis of COVID-19. To this end, we exploit a group testing (GT) scheme, which is used to find a small set of confirmed cases out of a large population. For the accurate detection of false positives and negatives, we propose a robust algorithm (RA) based on the maximum a posteriori probability (MAP). The key idea of the proposed RA is to exploit iterative detection to propagate beliefs to neighbor nodes by exchanging marginal probabilities between input and output nodes. As a result, we show that our proposed RA provides the benefit of being robust against noise in the GT schemes. In addition, we demonstrate the performance of our proposal with a number of tests and successfully find a set of infected samples in both noiseless and noisy GT schemes with different COVID-19 incidence rates. View Full-Text
Keywords: COVID-19; diagnosis; group testing; posterior probability; robust algorithm COVID-19; diagnosis; group testing; posterior probability; robust algorithm
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Seong, J.-T. Group Testing-Based Robust Algorithm for Diagnosis of COVID-19. Diagnostics 2020, 10, 396.

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