New Insights Into COVID-19 Transmission

Traditional epidemic models may underestimate real-world variability in case counts during certain epidemic phases. A new research paper in the journal PLOS Computational Biology by Exponent's Rachael Aber and colleagues from Oregon State University, Corvallis, investigates temporal and spatial variation in the dispersion of infectious disease case counts, focusing on how transmission patterns may have evolved during and after major COVID-19 surges in the U.S. The authors developed a statistical framework to quantify "dispersion" — a measure of how variable case counts are relative to their mean — in epidemiological time series.

The study found that case count dispersion rose markedly around the large COVID-19 surge in 2022. This means that as reported infections increased, their variability also grew, potentially indicating more uneven transmission (i.e., some areas or groups contributed disproportionately to total cases). Dispersion patterns varied across counties but showed consistent temporal trends, suggesting that shifts in variability reflect broad epidemiological processes rather than localized noise or data artifacts. The increase in dispersion contradicts theoretical expectations that high incidence should smooth variability. Instead, it suggests that transmission heterogeneity may have intensified — possibly playing a role in driving large surges. The observed changes in dispersion may stem from shifts in superspreading events, evolving population susceptibility, or reporting inconsistencies.

The results indicate that adjusting traditional homogeneous epidemic models to account for dispersion shifts could improve forecasts, helping public health officials anticipate changes in epidemic regime.

The study provides a novel method for tracking how variability in case counts changes over time and demonstrates that epidemic phases can be characterized not only by incidence but also by changes in case count dispersion. This approach could help identify when and why disease transmission heterogeneity increases, which is critical for planning and response efforts.

From the publication: "Traditional metrics used to quantify incidence patterns often overlook variability as an important characteristic of incidence time series."