ArticleScientific reports2022
Smart pooling: AI-powered COVID-19 informative group testing.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Assessment of the Effective Sensitivity of SARS-CoV-2 Sample Pooling Based on a Large-Scale Screening Experience: Retrospective Analysis.JMIR public health and surveillance · 2024Article
- Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19.Scientific reports · 2024Article
- Adaptive group testing strategy for infectious diseases using social contact graph partitions.Scientific reports · 2023Article
- Integrating Magnetic-Bead-Based Sample Extraction and Molecular Barcoding for the One-Step Pooled RT-qPCR Assay of Viral Pathogens without Retesting.Analytical chemistry · 2023Article
- Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML.Soft computing · 2023Article
- Sample pooling: burden or solution?Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2021Review
- Test Groups, Not Individuals: A Review of the Pooling Approaches for SARS-CoV-2 Diagnosis.Diagnostics (Basel, Switzerland) · 2021Review
- A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.IEEE access : practical innovations, open solutions · 2021Article
- Considerations for Pooled Testing of Employees for SARS-CoV-2.Journal of occupational and environmental medicine · 2021Article
Corrections and comments
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Authors and funding
19 authors.
Funding
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Abstract
Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.
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