# Viral Load Inference in Non-Adaptive Pooled Testing

arXiv: https://arxiv.org/abs/2403.09130 · HTML: https://mlsanity.com/paper/2403.09130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Compressed sensing reconstruction using Expectation Propagation](https://mlsanity.com/paper/1904.05777/) (2019)
- [Bayesian inference of infected patients in group testing with prevalence estimation](https://mlsanity.com/paper/2004.13667/) (2020)

## Abstract (arXiv)

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.