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Optimal multi-stage group partition for efficient coronavirus screening.

Yuvraj Gajpal1, S S Appadoo1, Victor Shi2

  • 1Asper School of Business, University of Manitoba, Winnipeg, MB R3T 5V4 Canada.

Annals of Operations Research
|February 28, 2022
PubMed
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This study introduces an optimal multi-stage group testing method for COVID-19 screening, significantly reducing test kit usage and time. The dynamic programming approach outperforms traditional binary tree methods, addressing global testing shortages.

Area of Science:

  • Operations Research
  • Public Health
  • Epidemiology

Background:

  • The COVID-19 pandemic caused global economic disruption due to widespread lockdowns.
  • Governments faced significant challenges with limited time and resources for widespread virus testing.
  • A shortage of testing kits exacerbated the public health crisis.

Purpose of the Study:

  • To develop an optimal method for multiple-stage group partitioning for coronavirus screening.
  • To minimize the total number of test kits required.
  • To reduce the number of testing stages needed for efficient screening.

Main Methods:

  • A dynamic programming approach was employed to devise the group partition strategy.
  • In each stage, populations were divided into subgroups for pooled testing.
Keywords:
COVID-19Dynamic programmingGroup testingMulti-stage group partitionOptimizationPandemics

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  • Positive subgroups were further subdivided in subsequent stages or tested individually.

    • Main Results:

    • The proposed multi-stage group partition scheme significantly minimizes test kit consumption.
    • The method reduces the overall number of testing stages required.
    • Numerical examples demonstrated the superiority of this method over the binary tree approach.

    Conclusions:

    • The developed scheme effectively addresses the test kit shortage problem.
    • This approach offers substantial time savings in screening processes.
    • The multi-stage sampling method provides a more efficient alternative for large-scale infectious disease testing.