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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Pandemic preparedness necessitates efficient testing strategies.
  • Pooled testing for SARS-CoV-2 (the virus causing COVID-19) can enhance testing capacity, particularly in low-prevalence scenarios.
  • Existing models for optimal pooled testing group size assume uniform disease distribution.

Purpose of the Study:

  • To investigate if stratifying pooled SARS-CoV-2 testing by age groups offers additional savings in test kits compared to traditional Dorfman pooling.
  • To determine if age-based pooling affects the estimation of optimal group size.

Main Methods:

  • A generalized Dorfman pooling model was adapted to incorporate age-specific data (0-19, 20-59, 60+ years).
  • Statistical weights were applied to age groups based on confirmed cases and testing data.
  • The impact on test kit usage and optimal group size was evaluated.

Main Results:

  • Pooling samples by age groups demonstrated a reduction in the number of tests required per diagnosed subject.
  • While individual savings are small, large-scale implementation can yield considerable resource optimization.
  • Greater test kit savings were observed in populations with significant variations in positivity rates across age segments.

Conclusions:

  • Age-stratified pooled testing for SARS-CoV-2 is a viable strategy for enhancing testing efficiency.
  • This approach can lead to cost savings in public health initiatives by optimizing the use of diagnostic resources.
  • The effectiveness of age-based pooling is particularly pronounced when disease prevalence differs notably among age demographics.