Group design in group testing for COVID-19 : A French case-study
| Autor: | Almeftah, Tifaout, Brotcorne, Luce, Cattaruzza, Diego, Fortz, Bernard, Keita, Kaba, Labbé, Martine, Ogier, Maxime, Semet, Frédéric |
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| Přispěvatelé: | Integrated Optimization with Complex Structure (INOCS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université libre de Bruxelles (ULB)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), INRIA - Centre Lille Nord Europe, Semet, Frédéric |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Constrained shortest path G.2 G.2.2 [INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO] COVID-19 Group design [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] Dynamic programming Methodology (stat.ME) Optimization and Control (math.OC) FOS: Mathematics Mathematics - Optimization and Control Statistics - Methodology Group testing |
| Zdroj: | [Research Report] INRIA-Centre Lille Nord Europe. 2020 |
| Popis: | Group testing is a screening strategy that involves dividing a population into several disjointed groups of subjects. In its simplest implementation, each group is tested with a single test in the first phase, while in the second phase only subjects in positive groups, if any, need to be tested again individually. In this paper, we address the problem of group testing design, which aims to determine a partition into groups of a finite population in such a way that cardinality constraints on the size of each group and a constraint on the expected total number of tests are satisfied while minimizing a linear combination of the expected number of false negative and false positive classifications. First, we show that the properties and model introduced by Aprahmian et al. can be extended to the group test design problem, which is then modeled as a constrained shortest path problem on a specific graph. We design and implement an ad hoc algorithm to solve this problem. On instances based on Sant\'e Publique France data on Covid-19 screening tests, the results of the computational experiments are very promising. |
| Databáze: | OpenAIRE |
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