| Paper ID | SS-4.5 |
| Paper Title |
CONTACT TRACING ENHANCES THE EFFICIENCY OF COVID-19 GROUP TESTING |
| Authors |
Ritesh Goenka, IIT Bombay, India; Shu-Jie Cao, ShanghaiTech University, China; Chau-Wai Wong, North Carolina State University, United States; Ajit Rajwade, IIT Bombay, India; Dror Baron, North Carolina State University, United States |
| Session | SS-4: Data Science Methods for COVID-19 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Data Science Methods for COVID-19 |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
