# A comparison of group testing architectures for COVID-19 testing

@article{Bottman2020ACO, title={A comparison of group testing architectures for COVID-19 testing}, author={Nathaniel Bottman and Y. Cooper and F. Janda}, journal={arXiv: Methodology}, year={2020} }

An important component of every country's COVID-19 response is fast and efficient testing -- to identify and isolate cases, as well as for early detection of local hotspots. For many countries, producing a sufficient number of tests has been a serious limiting factor in their efforts to control COVID-19 infections. Group testing is a well-established mathematical tool, which can provide a serious and rapid improvement to this situation. In this note, we compare several well-established group…

## 4 Citations

Group Testing During the COVID-19 Pandemic: Optimal Group Size Selection and Prevalence Control

- Computer Science
- 2020

This work explores two popular group testing methods, namely linear array (a.k.a Dorfman's procedure) and square array methods, and analyzes the optimal group size of a pooled sample that minimizes the group false negative number under a constraint of testing capacity.

Group Testing with Consideration of the Dilution Effect

- BusinessMathematics
- 2022

We propose a method of group testing by taking dilution effects into consideration. We estimate the dilution effect based on massively collected RT-PCR threshold cycle data and incorporate them into…

Modeling and computation of multistep batch testing for infectious diseases

- BusinessBiometrical journal. Biometrische Zeitschrift
- 2021

A mathematical model based on probability theory to optimize COVID‐19 testing by a multistep batch testing approach with variable batch sizes is proposed and results show that the method significantly reduces the false negative rate.

Design and Estimation for the Population Prevalence of Infectious Diseases

- BiologymedRxiv
- 2021

Bayesian multilevel regression and poststratification models that incorporate probability sampling designs, the sensitivity and specificity of a diagnostic test, and specimen pooling to obtain unbiased prevalence estimates are presented.

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