Fitting the fixed growth advantage model to Washington state data.

(a) Posterior expected cases without weekly seasonality in reporting rate. Gray bars are observed daily case counts, while blue lines are model inferences with 50%, 80% and 95% credible intervals. (b) Posterior expected cases by variant. Each colored line is a different variant with intervals of varying opacity showing 50%, 80% and 95% credible intervals. (c) Posterior variant frequency against observed sample frequency. Dots represent observed weekly frequencies in sequence data and each colored line is a different variant with shaded CIs. (d) Variant-specific effective reproduction numbers. (e) Posterior growth advantage by variant.

Fitting the GARW model to Washington state data.

(a) When assessing epidemic growth rates, we often compute a single effective reproduction number trajectory which is effectively an average over the all viruses in population. We show the posterior smoothed incidence over time as well as the average effective reproduction number. Gray bars are observed daily case counts, while black intervals are the posterior 50%, 80% and 95% credible intervals. (b-d) Epidemics are made of different variants which may differ in fitness. We show the posterior variant-specific smoothed incidence (b) as well as the average and variant-specific effective reproduction numbers (c-d). (e-f) Using case counts alongside sequences of different variants allows us to understand the proportion of different variants in the infected population.

Inferred effective reproduction numbers from GARW model in 34 states show consistent trends of variants across states.

Each panel shows a series of 34 trajectories, representing Rt through time for this variant across states. Shaded intervals show 50%, 80% and 95% credible intervals.

Using fixed growth advantage model, we infer growth advantages for 8 variants in 34 US states.

(a) Growth advantages for variants of concern. Each point is the median growth advantage inferred from a single state. (b) Same as (a) but with state medians visualized by variant.

Estimating variant growth advantages in 34 states using GARW model.

Each panel shows a series of 34 trajectories, representing Δv through time for variants across states. Histograms show the distribution of the variant’s growth advantage over time. Shaded intervals show 50%, 80% and 95% credible intervals.

Estimating growth advantages of Omicron sublineages relative to BA.1 in 33 US states.

(a) Time-varying growth advantages for BA.2, BA.2.12.1, BA.4, and BA.5 relative to BA.1 using the GARW model. Histograms denote the distribution of the variant growth advantages across all times. (b) Fixed growth advantages for Delta and BA.2 relative to BA.1 using fixed growth advantage model. (c) Same as (b) but with state medians visualized by variant.

Fitting the GARW model to California data.

Fitting the fixed growth advantage model to California data.

Fitting the GARW model to Florida data.

Fitting the fixed growth advantage model to Florida data.

Fitting the GARW model to Michigan data.

Fitting the fixed growth advantage model to Michigan data.

Fitting the GARW model to New York state data.

Fitting the fixed growth advantage model to New York state data.

Estimating variant growth advantages in various states using Multinomial Logistic Regression model assuming generation time Tg = 5.2.

(a) Growth advantages visualized by state. (b) Same as (a) but grouped by variant.

Sensitivity of effective reproduction number to changes in generation time.

(a) We vary the mean of Omicron generation time keeping a constant standard deviation 1.2 and plot against effective reproduction number estimates for Omicron in Washington state on February 1st, 2022 using our GARW model. (b) The same as (a), but we instead vary the standard deviation of Omicron generation time keeping a constant mean 3.1.

Sensitivity of epidemic growth rates to changes in generation time.

(a) We vary the mean of Omicron generation time keeping a constant standard deviation 1.2 and plot against exponential growth rates for Omicron in Washington state on February 1st, 2022 using our GARW model and assuming a Gamma-distributed generation time. (b) The same as (a), but we instead vary the standard deviation of Omicron generation time keeping a constant mean 3.1.

Sensitivity of growth advantages to changes in generation time.

(a) We vary the mean of Omicron generation time keeping a constant standard deviation 1.2 and plot against exponential growth rates for Delta in Washington state on July 1st, 2021 using our fixed growth model. (b) The same as (a), but we instead vary the standard deviation of Omicron generation time keeping a constant mean 3.2.