Author:
Aldridge Matthew,Ellis David
Abstract
AbstractWhen testing for a disease such as COVID-19, the standard method is individual testing: we take a sample from each individual and test these samples separately. An alternative is pooled testing (or ‘group testing’), where samples are mixed together in different pools, and those pooled samples are tested. When the prevalence of the disease is low and the accuracy of the test is fairly high, pooled testing strategies can be more efficient than individual testing. In this chapter, we discuss the mathematics of pooled testing and its uses during pandemics, in particular the COVID-19 pandemic. We analyse some one- and two-stage pooling strategies under perfect and imperfect tests, and consider the practical issues in the application of such protocols.
Publisher
Springer International Publishing
Reference44 articles.
1. M. Aldridge, Rates of adaptive group testing in the linear regime, in 2019 IEEE International Symposium on Information Theory (ISIT) (2019), pp. 236–240. https://doi.org/10.1109/ISIT.2019.8849712
2. M. Aldridge, Conservative two-stage group testing. May 2020. arXiv:2005.06617
3. M. Aldridge, D. Ellis, Pooled testing and its applications in the COVID-19 pandemic. (Extended version.) arXiv:2105.08845 (2021)
4. M. Aldridge, O. Johnson, J. Scarlett, Group testing: an information theory perspective. Foundations and Trends in Communications and Information Theory 15(3–4), 196–392 (2019)
5. D. Aran, Estimating real-world COVID-19 vaccine effectiveness in Israel using aggregated counts. Technical report, February 2021. medRxiv:2021.02.05.21251139
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献