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High-throughput Detection of Respiratory Pathogens in Animal Specimens by Nanoscale PCR
Published on: November 28, 2016
The Cornell COVID-19 Testing Laboratory: A Model to High-Capacity Testing Hubs for Infectious Disease Emergency
Melissa Laverack1, Rebecca L Tallmadge1, Roopa Venugopalan1
1Department of Population Medicine and Diagnostic Sciences, Animal Health Diagnostic Center (AHDC), College of Veterinary Medicine, Cornell COVID-19 Testing Laboratory (CCTL), Cornell University, Ithaca, NY 14853, USA.
Viruses
|July 29, 2023
Summary
The Cornell COVID-19 Testing Laboratory (CCTL) developed an automated, high-throughput workflow for rapid SARS-CoV-2 detection. This efficient system processed over 2 million samples, aiding in controlling campus and community spread.
Area of Science:
- * Public Health
- * Infectious Disease Diagnostics
- * Laboratory Automation
Background:
- * The COVID-19 pandemic necessitated rapid diagnostic solutions to curb SARS-CoV-2 transmission.
- * Existing healthcare systems faced significant strain due to the pandemic's scale.
- * High-throughput testing was crucial for managing outbreaks in communities and academic settings.
Purpose of the Study:
- * To describe the platform and workflow established at the Cornell COVID-19 Testing Laboratory (CCTL).
- * To detail the high-throughput testing strategy for clinical samples from a university and surrounding community.
- * To demonstrate efficient and rapid detection of SARS-CoV-2 for infection control.
Main Methods:
- * Implementation of a cost-effective, fully automated workflow for processing pooled samples.
- * Development of customized software for sample tracking, data management, and result reporting.
- * Integration of quality control dashboards and turnaround-time tracking for performance monitoring.
Main Results:
- * The CCTL workflow achieved high-throughput testing, processing over 8000 pooled samples daily.
- * Over 2 million samples were tested, enabling effective SARS-CoV-2 detection and control.
- * Automation of sample accessioning and pooling significantly enhanced laboratory efficiency.
Conclusions:
- * The developed automated workflow proved highly effective for large-scale SARS-CoV-2 testing.
- * The CCTL model offers a scalable solution for establishing regional high-capacity testing hubs.
- * This approach is valuable for infectious disease preparedness and emergency response strategies.
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