Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples

Area of Science:

• Epidemiology
• Genomic Surveillance
• Computational Biology

Background:

• Genomic data are crucial for tracking respiratory virus variants and their spread.
• Delays in genomic data submission limit prospective surveillance capabilities.
• Limited research exists on utilizing genomic data for evaluating aberration detection in surveillance systems.

Purpose of the Study:

• To establish 'gold standard' outbreak onset dates using genomic submission data.
• To evaluate the utility of genomic data for aberration detection in surveillance.
• To present a framework for using global genomic data for variant outbreak analysis.

Main Methods:

• Employed a Bayesian online change point detection algorithm (BOCP).
• Utilized submitted genome samples for respiratory viruses across multiple countries.
• Compared models with different data transformations and parameter values.

Main Results:

• BOCP effectively detected change points indicative of outbreak onset.
• Change point detection was robust to variations in parameter settings.
• Data transformations were identified as essential for accurate change point detection.

Conclusions:

• A framework using global genomic submission data can establish 'gold standard' outbreak onset dates.
• Genomic data, despite submission delays, can be valuable for evaluating surveillance systems.
• This approach enhances the understanding of new viral variant emergence and spread.