ArticleBulletin of the World Health Organization2020
Simulation of pooled-sample analysis strategies for COVID-19 mass testing.
Article in Bulletin of the World Health Organization, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial.JMIR public health and surveillance · 2023Trial
- Optimal pooling strategies for respiratory virus testing: A comparative cost-effectiveness analysis.PLOS global public health · 2026Article
- Feasibility of molecular-based testing strategies for SARS-CoV-2-screening in workplaces: a systematic review and reporting checklist.BMJ public health · 2026Article
- Comparison of RT-PCR cycle threshold values between individual and pooled SARS-CoV-2 infected nasopharyngeal swab specimens.Narra J · 2024Article
- Optimizing the detection of emerging infections using mobility-based spatial sampling.International journal of applied earth observation and geoinformation : ITC journal · 2024Article
- Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19.Scientific reports · 2024Article
- Optimization of Screening Strategies for COVID-19: Scoping Review.JMIR public health and surveillance · 2024Article
- An integrated microfluidic platform for nucleic acid testing.Microsystems & nanoengineering · 2024Article
- Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models.Scientific reports · 2023Article
- Adaptive group testing strategy for infectious diseases using social contact graph partitions.Scientific reports · 2023Article
- Article
- Accelerating the Laboratory Testing Capacity through Saliva Pooling Prior to Direct RT-qPCR for SARS-CoV-2 Detection.Diagnostics (Basel, Switzerland) · 2022Article
- Effective screening strategies for safe opening of universities under Omicron and Delta variants of COVID-19.Scientific reports · 2022Article
- Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach.Automation in construction · 2022Article
- Comparison of extraction-based and elution-based polymerase chain reaction testing, and automated and rapid antigen testing for the diagnosis of severe acute respiratory syndrome coronavirus 2.Journal of medical virology · 2022Article
- Strategies for Scaling up SARS-CoV-2 Molecular Testing Capacity.Clinics in laboratory medicine · 2022Review
- Evaluation of conventional and point-of-care real-time RT-PCR tests for the detection of SARS-CoV-2 through a pooled testing strategy.Journal of clinical laboratory analysis · 2022Article
- Pooled sample testing for COVID-19 diagnosis: Evaluation of bi-directional matrix pooling strategies.Journal of virological methods · 2022Article
- Anticipating human resilience and vulnerability on the path to 2030: What can we learn from COVID-19?Futures · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate two pooled-sample analysis strategies (a routine high-throughput approach and a novel context-sensitive approach) for mass testing during the coronavirus disease 2019 (COVID-19) pandemic, with an emphasis on the number of tests required to screen a population.
methodsWe used Monte Carlo simulations to compare the two testing strategies for different infection prevalences and pooled group sizes. With the routine high-throughput approach, heterogeneous sample pools are formed randomly for polymerase chain reaction (PCR) analysis. With the novel context-sensitive approach, PCR analysis is performed on pooled samples from homogeneous groups of similar people that have been purposively formed in the field. In both approaches, all samples contributing to pools that tested positive are subsequently analysed individually.
findingsBoth pooled-sample strategies would save substantial resources compared to individual analysis during surge testing and enhanced epidemic surveillance. The context-sensitive approach offers the greatest savings: for instance, 58-89% fewer tests would be required for a pooled group size of 3 to 25 samples in a population of 150 000 with an infection prevalence of 1% or 5%. Correspondingly, the routine high-throughput strategy would require 24-80% fewer tests than individual testing.
conclusionPooled-sample PCR screening could save resources during COVID-19 mass testing. In particular, the novel context-sensitive approach, which uses pooled samples from homogeneous population groups, could substantially reduce the number of tests required to screen a population. Pooled-sample approaches could help countries sustain population screening over extended periods of time and thereby help contain foreseeable second-wave outbreaks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.