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Positively Correlated Samples Save Pooled Testing Costs.

Yi-Jheng Lin1, Che-Hao Yu1, Tzu-Hsuan Liu1

  • 1Institute of Communications EngineeringNational Tsing Hua University Hsinchu 300044 Taiwan.

IEEE Transactions on Network Science and Engineering
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

Group testing for COVID-19 can be more cost-effective by accounting for positive correlations between individuals. Exploiting these correlations with the Dorfman two-stage method and a social graph algorithm further reduces testing costs.

Keywords:
COVID-19Markov modulated processesgroup testingregenerative processessocial networks

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Group testing offers cost savings for mass testing, particularly for infectious diseases like COVID-19.
  • Existing group testing models often assume sample independence, which is unrealistic for contagious diseases with familial transmission.
  • Positive correlations in test results within groups can arise due to disease transmission patterns.

Purpose of the Study:

  • To demonstrate that positive correlations can enhance cost reduction in group testing for COVID-19.
  • To rigorously prove the efficacy of the Dorfman two-stage method under positive correlation.
  • To develop a novel algorithm for pooled testing that leverages social network structures.

Main Methods:

  • Mathematical modeling to analyze the impact of positive correlation on group testing.
  • Application and rigorous proof of the Dorfman two-stage method for correlated samples.
  • Development of a hierarchical agglomerative algorithm using social graphs for pooled testing.

Main Results:

  • Positive correlation between samples within a group can lead to further cost reductions using the Dorfman two-stage method.
  • The proposed hierarchical agglomerative algorithm, utilizing social graphs, achieved 20-35% cost reduction compared to random pooling.
  • The algorithm effectively incorporates social contact information into the pooling strategy.

Conclusions:

  • Accounting for positive correlations in group testing is crucial for optimizing COVID-19 mass testing strategies.
  • The Dorfman two-stage method is robust and can be further optimized with correlated data.
  • Social graph-informed pooled testing offers a significant advancement in cost-effective disease surveillance.