Metadata about: We investigate group based testing strategy targeted to identify infected patients by making use of a medical test which equally well applies to single and pooled samples. We demonstrate that, under assumed setting, quick sort grounded testing algorithm allows to reduce average costs, and the reduction is very significant when the infection percentage is low. Although the basic idea of test sampling is known, our major novelty is the rigorous treatment of the model. Another interesting insight following rigorous analysis is that an average number of tests per one individual scales like entropy of the prevalence of infection. One more reason for the paper is the context: taking into account the current situation with the coronavirus, dissemination of renowned ideas and the optimisation of algorithms can be of a great importance and of economical benefit.

About: We investigate group based testing strategy targeted to identify infected patients by making use of a medical test which equally well applies to single and pooled samples. We demonstrate that, under assumed setting, quick sort grounded testing algorithm allows to reduce average costs, and the reduction is very significant when the infection percentage is low. Although the basic idea of test sampling is known, our major novelty is the rigorous treatment of the model. Another interesting insight following rigorous analysis is that an average number of tests per one individual scales like entropy of the prevalence of infection. One more reason for the paper is the context: taking into account the current situation with the coronavirus, dissemination of renowned ideas and the optimisation of algorithms can be of a great importance and of economical benefit.

an Entity references as follows:

Graph IRI	Count
http://ns.inria.fr/covid19/graph/entityfishing	3
http://ns.inria.fr/covid19/graph/articles	3

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