Viral Load Inference in Non-Adaptive Pooled Testing
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909136712630272 |
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| author | Sheikh, Mansoor Saad, David |
| author_facet | Sheikh, Mansoor Saad, David |
| contents | Medical diagnostic testing can be made significantly more efficient using pooled testing protocols. These typically require a sparse infection signal and use either binary or real-valued entries of O(1). However, existing methods do not allow for inferring viral loads which span many orders of magnitude. We develop a message passing algorithm coupled with a PCR (Polymerase Chain Reaction) specific noise function to allow accurate inference of realistic viral load signals. This work is in the non-adaptive setting and could open the possibility of efficient screening where viral load determination is clinically important. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_09130 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Viral Load Inference in Non-Adaptive Pooled Testing Sheikh, Mansoor Saad, David Statistical Mechanics Applications Machine Learning Medical diagnostic testing can be made significantly more efficient using pooled testing protocols. These typically require a sparse infection signal and use either binary or real-valued entries of O(1). However, existing methods do not allow for inferring viral loads which span many orders of magnitude. We develop a message passing algorithm coupled with a PCR (Polymerase Chain Reaction) specific noise function to allow accurate inference of realistic viral load signals. This work is in the non-adaptive setting and could open the possibility of efficient screening where viral load determination is clinically important. |
| title | Viral Load Inference in Non-Adaptive Pooled Testing |
| topic | Statistical Mechanics Applications Machine Learning |
| url | https://arxiv.org/abs/2403.09130 |