# Contact Tracing Enhances the Efficiency of COVID-19 Group Testing

arXiv: https://arxiv.org/abs/2011.14186 · HTML: https://mlsanity.com/paper/2011.14186/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing](https://mlsanity.com/paper/2305.07639/) (2023)
- [Compressive Recovery of Signals Defined on Perturbed Graphs](https://mlsanity.com/paper/2402.07637/) (2024)

## Abstract (arXiv)

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 non-adaptive/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 non-adaptive group testing, ours is the first work to explore and demonstrate how CT SI can further improve group testing performance.