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Abstract Background: Results: To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to e ciently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the rst stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution e ects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% on the only complete real-world qPCR group testing dataset from Israel, while maintaining a negligible false negative rate. Conclusion: In conclusion, our adaptive SQGT approach, utilizing qPCR data and dynamic threshold adjustments, o ers a promising solution for e cient population screening. With a reduction in the number of tests and minimal false negatives, SQGT holds potential to enhance disease control and testing strategies on a global scale. Keywords: Group testing, Pooled testing, Semiquantitative group testing, qPCR, Ct values, Viral load, COVID-19
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Finding a Burst of Positives via Nonadaptive Semiquantitative Group Testing
Motivated by testing for pathogenic diseases we con- sider a new nonadaptive group testing problem for which: (1) positives occur within a burst, capturing the fact that infected test subjects often come in clusters, and (2) that the test outcomes arise from semiquantitative measurements that provide coarse information about the number of positives in any tested group. Our model generalizes prior work on detecting a single burst with classical group testing [1] to the setting of semiquantitative group testing (SQGT) [2]. Speci cally, we study the setting where the burst-length l is known and the semiquantitative tests provide potentially nonuniform estimates on the number of positives in a test group. The estimates represent the index of a quantization bin containing the (exact) total number of positives, for arbitrary thresholds η1,...,ηs. Interestingly, we show that the minimum number of tests needed for burst identi cation is essentially only a function of the largest threshold ηs. In this context, our main result is an order-optimal test scheme that can recover any burst of length l using roughly \ell/2\eta + log (n) measurements. This suggests that 2ηs s+1 a large saturation level ηs is more important than nely quantized information when dealing with bursts. We also provide results for related modeling assumptions and specialized choices of thresholds.
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- Award ID(s):
- 2107344
- PAR ID:
- 10549928
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 9781665475556
- Format(s):
- Medium: X
- Location:
- Taipei, Taiwan
- Sponsoring Org:
- National Science Foundation
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