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Identifying patients from a large number of people by a small number of tests

Authors: Xie, Y.H.;

Identifying patients from a large number of people by a small number of tests

Abstract

An approach is proposed for screening patients from more people with less tests. It can be used for various diseases, and one of the direct application is the suppression of pandemic (e.g., coronavirus pneumonia). In this approach, people are divided into several groups, and each group has multiple people. For each group, the samples (e.g., respiratory secretions) of all the people are mixed to generate a merged sample. All the people can be excluded if the merged sample indicates no illness. If the merged sample indicates illness, the group will be divided into smaller groups, and then handled recursively in the same way. By doing so, negative groups are excluded gradually, and the group size becomes smaller and smaller. Finally, each group has only one person. This approach is especially suitable for the case when only small percentage of people are ill (e.g., infected).

Presently, a pandemic is threatening public health. Limited by the time, we did not investigate literatures extensively. Instead, we focused on the application and effectiveness of this approach. (By the way, sometimes there is no need to identify every patient, but only need to identify relatively small ranges instead. In that case, the proposed approach can be stopped before the whole procedure finishes.)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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