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Viral Load Inference in Non-Adaptive Pooled Testing (2403.09130v1)
Published 14 Mar 2024 in cond-mat.stat-mech, stat.AP, and stat.ML
Abstract: 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.
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