ArticleIEEE transactions on network science and engineering2021
Positively Correlated Samples Save Pooled Testing Costs.
Article in IEEE transactions on network science and engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Optimization of Screening Strategies for COVID-19: Scoping Review.JMIR public health and surveillance · 2024Article
- Pooled testing of traced contacts under superspreading dynamics.PLoS computational biology · 2022Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The group testing approach, which achieves significant cost reduction over the individual testing approach, has received a lot of interest lately for massive testing of COVID-19. Many studies simply assume samples mixed in a group are independent. However, this assumption may not be reasonable for a contagious disease like COVID-19. Specifically, people within a family tend to infect each other and thus are likely to be positively correlated. By exploiting positive correlation, we make the following two main contributions. One is to provide a rigorous proof that further cost reduction can be achieved by using the Dorfman two-stage method when samples within a group are positively correlated. The other is to propose a hierarchical agglomerative algorithm for pooled testing with a social graph, where an edge in the social graph connects frequent social contacts between two persons. Such an algorithm leads to notable cost reduction (roughly 20-35%) compared to random pooling when the Dorfman two-stage algorithm is applied.
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