Efficient COVID-19 testing via contextual model based compressive sensing

作者：

Highlights：

• To prevent COVID-19 contagion, it is essential to detect suspect people as soon as possible.

• Model-based Compressive sensing can determine affected individuals with a lower number of test

• Group testing is not applicable is the condition that the ratio of affected/non-affected persons is high, such as COVID-19 test.

• The contextual information (background disease, lifestyle, age, fever, etc.) can be used to build a graph model which using in model-based compressive sensing.

摘要

•To prevent COVID-19 contagion, it is essential to detect suspect people as soon as possible.•Model-based Compressive sensing can determine affected individuals with a lower number of test•Group testing is not applicable is the condition that the ratio of affected/non-affected persons is high, such as COVID-19 test.•The contextual information (background disease, lifestyle, age, fever, etc.) can be used to build a graph model which using in model-based compressive sensing.

论文关键词：COVID-19,Graph signal model,Group testing,Model-based compressive sensing

论文评审过程：Received 5 November 2020, Revised 11 August 2021, Accepted 11 August 2021, Available online 13 August 2021, Version of Record 22 August 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108253