Noisy Pooled PCR for Virus Testing

Abstract

Fast testing can help mitigate the coronavirus disease 2019 pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!

Effectiveness of Sample Pooling Strategies for SARS-CoV-2 Mass Screening by RT-PCR: A Scoping Review

“…It is clear from above that pooling strategy can be effectively applied for the detection of SARS-CoV-2 by RT-qPCR, as supported by other studies. [20][21][22][23][24] Optimal pool size has to be determined by the level/rate of infection in that particular population. Currently, positivity rate (p) in India is 6.1; although due to diversity in population distribution, there is great variance in this rate from one state to another.…”

Contact Tracing Enhances the Efficiency of Covid-19 Group Testing

“…Pooled or group testing has been suggested for improving testing efficiencies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Group testing involves mixing a subset of n individual samples into m < n pools.…”

“…We consider two signal and noise models. Model M1: A binary noise model used by Zhu et al [6], where x is binary, w = Ax is an auxiliary vector, and the RG and SJC have made equal contributions. AR acknowledges support from SERB Grant #10013890, IITB-WRCB Grant #DONWR04-002, and DST-Rakshak grant #DST0000-005.…”

“…Algorithms that estimate x from y and A [8,12] rely primarily on the sparsity of x, which is a valid assumption for COVID-19 due to low prevalence rates [18]. Zhu et al [6] also exploit probabilistic information such as the prevalence rate and structure in x, and state the potential benefits of using side information (SI). Specific forms of SI include individuals' symptoms and family structure [9].…”

“…Zhu et al [6] also exploit probabilistic information such as the prevalence rate and structure in x, and state the potential benefits of using side information (SI). Specific forms of SI include individuals' symptoms and family structure [9]. Finally, Nikolopoulos et al independently observed that taking into account community structure can improve the performance of group testing [11,15]; these works focused on the encoder design and used different contact models.…”