Group testing performance evaluation for SARS-CoV-2 massive scale screening and testing.

Background: The capacity of the current molecular testing convention does not allow high-throughput and community level scans of COVID-19 infections. The diameter in the current paradigm of shallow tracing is unlikely to reach the silent clusters that might be as important as the symptomatic cases i...

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Publicado en:BMC Medical Research Methodology Vol. 20; no. 1; pp. 1 - 12
Autor principal: Nalbantoglu, Ozkan Ufuk
Formato: Journal Article
Publicado: BioMed Central 7/2/2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 7/2/2020
      vid: 20
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      pub: BioMed Central
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        144356508
        144356508
        NLM32615934
        10.1186/s12874-020-01048-1
        NLM32615934
        144356508
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        atl: Group testing performance evaluation for SARS-CoV-2 massive scale screening and testing.
      aug:
        au: Nalbantoglu, Ozkan Ufuk
        affil: Department of Computer Engineering, Erciyes University, 38039, Kayseri, Turkey
      sug:
        subj:
          Health Screening Methods
          COVID-19 Diagnosis
          Pneumonia, Viral Diagnosis
          Computer Simulation
          Algorithms
          Random Assignment
          Disease Outbreaks
          Clinical Assessment Tools
      ab: Background: The capacity of the current molecular testing convention does not allow high-throughput and community level scans of COVID-19 infections. The diameter in the current paradigm of shallow tracing is unlikely to reach the silent clusters that might be as important as the symptomatic cases in the spread of the disease. Group testing is a feasible and promising approach when the resources are scarce and when a relatively low prevalence regime is observed on the population.Methods: We employed group testing with a sparse random pooling scheme and conventional group test decoding algorithms both for exact and inexact recovery.Results: Our simulations showed that significant reduction in per case test numbers (or expansion in total test numbers preserving the number of actual tests conducted) for very sparse prevalence regimes is available. Currently proposed COVID-19 group testing schemes offer a gain up to 15X-20X scale-up. There is a good probability that the required scale up to achieve massive scale testing might be greater in certain scenarios. We investigated if further improvement is available, especially in sparse prevalence occurrence where outbreaks are needed to be avoided by population scans.Conclusion: Our simulations show that sparse random pooling can provide improved efficiency gains compared to conventional group testing or Reed-Solomon error correcting codes. Therefore, we propose that special designs for different scenarios could be available and it is possible to scale up testing capabilities significantly.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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