2021 article
CONTACT TRACING ENHANCES THE EFFICIENCY OF COVID-19 GROUP TESTING
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), pp. 8168–8172.
author keywords: Contact tracing; nonadaptive group testing; compressed sensing; overlapping group LASSO; generalized approximate message passing (GAMP)
TL;DR:
This work uses side information (SI) collected from contact tracing (CT) within nonadaptive/single-stage group testing algorithms to explore and demonstrate how CT SI can further improve group testing performance.
(via Semantic Scholar)
open access via ieeexplore.ieee.org (publisher PDF)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 29, 2021
Group testing can save testing resources in the context of the ongoing COVID-19 pandemic. In group testing, we are given n samples, one per individual, and arrange them into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we use side information (SI) collected from contact tracing (CT) within nonadaptive/single-stage group testing algorithms. We generate data by incorporating CT SI and characteristics of disease spread between individuals. These data are fed into two signal and measurement models for group testing, where numerical results show that our algorithms provide improved sensitivity and specificity. While Nikolopoulos et al. utilized family structure to improve nonadaptive group testing, ours is the first work to explore and demonstrate how CT SI can further improve group testing performance.