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Extract growth advantage estimates

Source: R/growth-advantage.R
growth_advantage.Rd

Computes relative fitness of each lineage from a fitted model. Supports four output formats for different use cases.

Usage

growth_advantage(
  fit,
  type = c("growth_rate", "relative_Rt", "selection_coefficient", "doubling_time"),
  generation_time = NULL,
  ci_level = NULL
)

Arguments

fit

An lfq_fit object returned by fit_model().

type

Output type:

  • "growth_rate" (default): raw growth rate delta per time_scale days (typically per week).

  • "relative_Rt": relative effective reproduction number. Requires generation_time.

  • "selection_coefficient": relative Rt minus 1. Requires generation_time.

  • "doubling_time": days for frequency ratio vs pivot to double (positive = growing) or halve (negative = declining).

generation_time

Mean generation time in days. Required for type = "relative_Rt" and "selection_coefficient". Common values: SARS-CoV-2 approximately 5 days, influenza approximately 3 days.

ci_level

Confidence level for intervals. Default uses the level from the fitted model.

Value

A tibble with columns:

lineage

Lineage name.

estimate

Point estimate.

lower

Lower confidence bound.

upper

Upper confidence bound.

type

Type of estimate.

pivot

Name of pivot (reference) lineage.

Details

The "relative_Rt" and "selection_coefficient" types use the Piantham approximation (Piantham et al. 2022, doi:10.3390/v14112556 ), which assumes:

  1. Variants are in their exponential growth phase (not saturating due to population-level immunity).

  2. All variants share the same generation time distribution.

  3. Growth advantage reflects transmissibility differences; immune escape is not separately identified.

Confidence intervals for "relative_Rt" are computed in log-space (Wald intervals on log-Rt), which is more accurate than the linear delta method for ratios.

References

Piantham C, Linton NM, Nishiura H (2022). Predicting the trajectory of replacements of SARS-CoV-2 variants using relative reproduction numbers. Viruses, 14(11):2556. doi:10.3390/v14112556

Abousamra E, Figgins M, Bedford T (2024). Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency. PLoS Computational Biology, 20(9):e1012443. doi:10.1371/journal.pcbi.1012443

Examples

sim <- simulate_dynamics(
  n_lineages = 3,
  advantages = c("JN.1" = 1.3, "KP.3" = 0.9),
  n_timepoints = 15, seed = 42
)
fit <- fit_model(sim, engine = "mlr")

# Growth rates per week
growth_advantage(fit, type = "growth_rate")
#> # A tibble: 3 × 6
#>   lineage estimate  lower upper type        pivot
#>   <chr>      <dbl>  <dbl> <dbl> <chr>       <chr>
#> 1 JN.1       0.364 0.338  0.390 growth_rate KP.3 
#> 2 KP.3       0     0      0     growth_rate KP.3 
#> 3 ref        0.112 0.0834 0.141 growth_rate KP.3 

# Relative Rt (needs generation time)
growth_advantage(fit, type = "relative_Rt", generation_time = 5)
#> # A tibble: 3 × 6
#>   lineage estimate lower upper type        pivot
#>   <chr>      <dbl> <dbl> <dbl> <chr>       <chr>
#> 1 JN.1        1.30  1.27  1.32 relative_Rt KP.3 
#> 2 KP.3        1     1     1    relative_Rt KP.3 
#> 3 ref         1.08  1.06  1.11 relative_Rt KP.3