Optimal multi-stage group partition for efficient coronavirus screening.

Yuvraj Gajpal, S S Appadoo, Victor Shi, Guoping Hu

Open access · bronzeAbstract read

In one paragraph

Article in Annals of operations research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.4field-weighted citation impact, top 48% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

Pooled Testing Strategies for SARS-CoV-2 diagnosis: A comprehensive review.Diagnostic microbiology and infectious disease · 2021
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Yuvraj GajpalAsper School of Business, University of Manitoba, Winnipeg, MB R3T 5V4 Canada.

S S AppadooAsper School of Business, University of Manitoba, Winnipeg, MB R3T 5V4 Canada.

Victor ShiLazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, ON N2L 3C5 Canada.

Guoping HuSchool of Business Administration, Southwestern University of Finance and Economics, Chengdu, China.ORCID 0000-0001-5105-9107

University of Manitoba · CASouthwestern University of Finance and Economics · CNWilfrid Laurier University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of COVID-19 has affected the economy worldwide due to entire countries being on lockdown. This has been highly challenging for governments facing constraints in terms of time and resources related to the availability of testing kits for the virus. This paper develops an optimal method for multiple-stage group partition for coronavirus screening using a dynamic programming approach. That is, in each stage, a group of people is divided into a certain number of subgroups, each will be tested as a whole. Only the subgroup(s) tested positive will be further divided into smaller subgroups in the next stage or individuals at the last stage. Our multiple-stage group partition scheme is able to minimize the total number of test kits and the number of stages. Our scheme can help solve the test kit shortage problem and save time. Finally, numerical examples with useful managerial insights for further investigation are presented. The results confirm the advantages of the multi-stage sampling method over the existing binary tree method.

Indexed as

COVID-19Dynamic programmingGroup testingMulti-stage group partitionOptimizationPandemics

Identifiers

PMID35221417

DOI10.1007/s10479-022-04543-4

PMCPMC8860262

OpenAlexW4220672502

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.