Group testing for connected communities

Pavlos Nikolopoulos, Sundara Rajan Srinivasavaradhan, Tao Guo, Christina Fragouli, Suhas Diggavi
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2341-2349, 2021.

Abstract

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depends on the community (s)he participates in. Use cases include families, students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that if we design the testing strategy taking into account the community structure, we can significantly reduce the number of tests needed for adaptive and non-adaptive group testing, and can improve the reliability in cases where tests are noisy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-nikolopoulos21a, title = { Group testing for connected communities }, author = {Nikolopoulos, Pavlos and Rajan Srinivasavaradhan, Sundara and Guo, Tao and Fragouli, Christina and Diggavi, Suhas}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {2341--2349}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/nikolopoulos21a/nikolopoulos21a.pdf}, url = {https://proceedings.mlr.press/v130/nikolopoulos21a.html}, abstract = { In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depends on the community (s)he participates in. Use cases include families, students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that if we design the testing strategy taking into account the community structure, we can significantly reduce the number of tests needed for adaptive and non-adaptive group testing, and can improve the reliability in cases where tests are noisy. } }
Endnote
%0 Conference Paper %T Group testing for connected communities %A Pavlos Nikolopoulos %A Sundara Rajan Srinivasavaradhan %A Tao Guo %A Christina Fragouli %A Suhas Diggavi %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-nikolopoulos21a %I PMLR %P 2341--2349 %U https://proceedings.mlr.press/v130/nikolopoulos21a.html %V 130 %X In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depends on the community (s)he participates in. Use cases include families, students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that if we design the testing strategy taking into account the community structure, we can significantly reduce the number of tests needed for adaptive and non-adaptive group testing, and can improve the reliability in cases where tests are noisy.
APA
Nikolopoulos, P., Rajan Srinivasavaradhan, S., Guo, T., Fragouli, C. & Diggavi, S.. (2021). Group testing for connected communities . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:2341-2349 Available from https://proceedings.mlr.press/v130/nikolopoulos21a.html.

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