On Old Dominion University Week: How fast can a viral variant spread?
Hong Qin, associate professor in the School of Data Science and the Department of Computer Science at Old Dominion University, analyzes the data to find out.
Faculty Bio:
Hong Qin is an Associate Professor in the School of Data Science and the Department of Computer Science at Old Dominion University. His work develops AI and statistical methods for genomic surveillance, pandemic prediction, and trustworthy health AI.
Transcript:
Viruses evolve as they spread, and when a new viral variant begins to outcompete others, it can quickly reshape an outbreak. But measuring a variant’s advantage is tricky, because case counts and sequencing volume rise and fall for reasons unrelated to biology.
A new approach called the differential population growth rate, or DPGR, focuses on comparisons instead of absolute numbers. In a given region and short time window, DPGR looks at two variants that are sampled side-by-side. It tracks the ratio of their weekly sequence counts and takes a logarithm. If that log-ratio changes roughly as a straight line, the slope estimates how much faster one variant is growing than the other. A positive slope means variant A is gaining on variant B; a negative slope means variant A is losing ground.
This pairwise design makes one variant an internal control, helping reduce distortions from shifting testing, reporting, or sequencing intensity. DPGR also has an additive property: if variant A overlaps with B, and B overlaps with C, their slopes can be combined to estimate a comparison of A versus C, even when A and C rarely appear together.
Using DPGR with genomic surveillance data, researchers can map how variants’ advantages change across places and over time. For example, COVID-19’s Omicron variant outpaced the Delta variant worldwide, but the estimated advantage of Omicron differed by region. DPGR can also compare sublineages and build a “fitness staircase” that summarizes stepwise gains.
The result? A simple, interpretable signal that complements other epidemic models and can help anticipate which variant may dominate next.
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