Pooling of coronavirus tests under unknown prevalence.

Area of Science:

• Epidemiology
• Medical Diagnostics
• Public Health

Background:

• Diagnostic testing is crucial for managing the Covid-19 pandemic.
• Current testing capacities are limited, hindering effective disease control.
• Pooled testing offers a strategy to enhance testing throughput.

Purpose of the Study:

• To model pooled testing for Covid-19 using a double-average model.
• To determine the optimal pool size for Covid-19 testing.
• To evaluate the impact of test errors on pooled testing strategies.

Main Methods:

• Development of a double-average model for pooled testing.
• Simulation of pooled testing protocols under various conditions.
• Analysis of the influence of pool size and test error rates.

Main Results:

• The optimal pool size for Covid-19 testing was identified as three to five.
• Pooled testing was found to be effective in increasing testing capacity.
• Under specific assumptions, pooled testing can reduce false positive results.

Conclusions:

• Pooled testing is a viable strategy to augment Covid-19 diagnostic capacity.
• Optimizing pool size is essential for maximizing efficiency and accuracy.
• Further research into pooled testing protocols can aid pandemic response efforts.