Efficient and effective single-step screening of individual samples for SARS-CoV-2 RNA using multi-dimensional...

Area of Science:

• Molecular diagnostics
• Infectious disease surveillance
• Public health preparedness

Background:

• Molecular assays are crucial for detecting viral pathogens with high accuracy.
• Implementing widespread molecular testing during public health emergencies (e.g., COVID-19) faces resource and technical challenges.
• Efficient and scalable surveillance strategies are vital for responding to novel health threats.

Purpose of the Study:

• To present a scalable, non-adaptive pooled-sample screening protocol for infectious disease surveillance.
• To validate the protocol's performance using clinical specimens for SARS-CoV-2 detection.
• To demonstrate the advantages of the proposed screening method for large-volume, sustained testing.

Main Methods:

• A non-adaptive pooled-sample screening protocol utilizing Bayesian inference was developed.
• The protocol yields a reportable outcome for each individual sample in a single testing step, eliminating the need for confirmation testing.
• Validation was performed using real-time reverse transcription polymerase chain reaction (RT-PCR) assays on clinical specimens for SARS-CoV-2.

Main Results:

• The pooled-sample screening protocol demonstrated high sample throughput and faster time to results.
• The Bayesian inference algorithm achieved excellent sensitivity and specificity comparable to individual testing metrics.
• The method eliminates the need for retesting stored samples, streamlining the surveillance process.

Conclusions:

• The presented pooled-sample screening strategy is easily scalable and supports sustained, large-volume infectious disease surveillance.
• This protocol offers significant advantages in terms of efficiency, speed, and performance for molecular testing during public health crises.
• The Bayesian inference approach provides a robust and reliable method for rapid detection of viral pathogens.