Optimization of group size in pool testing strategy for SARS-CoV-2: A simple mathematical model

Area of Science:

• Public Health
• Epidemiology
• Biotechnology

Background:

• The COVID-19 pandemic necessitates rapid scaling of diagnostic testing capacity globally.
• Pool testing, analyzing multiple samples per RT-PCR kit, offers a potential solution to increase throughput.
• Optimizing pool sizes is crucial for maximizing efficiency and resource conservation.

Purpose of the Study:

• To develop a mathematical model for determining optimal pooled sample sizes for COVID-19 RT-PCR testing.
• To estimate potential test savings based on varying prevalence rates and pool sizes.
• To provide a context-specific framework for implementing pool testing strategies.

Main Methods:

• A simple mathematical model was developed to calculate optimal pooled sample sizes.
• The model considers a scenario where negative pools are not retested, but positive pools undergo individual retesting.
• Simulations were run for different prevalence rates (e.g., 10%, 20%) to assess test savings.

Main Results:

• The model predicts optimal group sizes ranging from 3 to 11 subjects.
• At 10% prevalence, using groups of four can save 40.6% of tests.
• At 20% prevalence, groups of three save 17.9% of tests; effectiveness decreases with higher prevalences.

Conclusions:

• Pool testing is a valuable strategy for boosting COVID-19 testing capacity, particularly in low-prevalence settings.
• The strategy is most effective for individuals with low clinical suspicion.
• Further research is needed to determine maximum group sizes without compromising RT-PCR sensitivity.