Population-level selective pressure from variant dynamics — selective

Computes a population-level selective pressure metric from genomic surveillance data alone, without requiring case counts or epidemiological data. The metric quantifies how rapidly the variant landscape is shifting and serves as an early warning signal for epidemic growth that is robust to case underreporting.

Usage

selective_pressure(fit, method = c("mean", "variance"))

Arguments

fit: An lfq_fit object from fit_model.
method: Aggregation method: "mean" (default) for the frequency-weighted mean growth rate, or "variance" for the frequency-weighted variance of growth rates (higher variance indicates stronger selection).

Value

A tibble with columns:

date: Date.
pressure: Selective pressure value.
dominant_lineage: Lineage with highest frequency.
dominant_freq: Frequency of dominant lineage.

Details

The approach follows the framework of Figgins and Bedford (2025), where selective pressure is defined as the variance-weighted mean growth rate across circulating lineages: $$S(t) = \sum_v p_v(t) \cdot \delta_v$$ This represents the expected rate at which the population-average fitness is increasing, measured entirely from sequence data.

When method = "mean", the metric is positive when fitter- than-average lineages are increasing in frequency, indicating population-level adaptation. A sustained positive value precedes epidemic growth because it means the effective reproduction number of the average circulating virus is increasing.

When method = "variance", the metric captures the heterogeneity of fitness across co-circulating lineages. High variance indicates strong directional selection; low variance indicates near-neutral drift.

This metric requires only genomic surveillance data. It does not require case counts, hospitalisations, or wastewater data, making it applicable in settings where epidemiological reporting is incomplete or delayed.

References

Figgins MD, Bedford T (2025). Jointly modeling variant frequencies and case counts to estimate relative variant severity. medRxiv. doi:10.1101/2024.12.02.24318334

Examples

# \donttest{
sim <- simulate_dynamics(n_lineages = 4,
  advantages = c("A" = 1.4, "B" = 1.1, "C" = 0.8),
  n_timepoints = 15, seed = 1)
fit <- fit_model(sim, engine = "mlr")
sp <- selective_pressure(fit)
sp
#> # A tibble: 15 × 4
#>    date       pressure dominant_lineage dominant_freq
#>    <date>        <dbl> <chr>                    <dbl>
#>  1 2026-04-13    0.276 A                        0.260
#>  2 2026-04-20    0.317 A                        0.338
#>  3 2026-04-27    0.356 A                        0.421
#>  4 2026-05-04    0.392 A                        0.505
#>  5 2026-05-11    0.424 A                        0.586
#>  6 2026-05-18    0.451 A                        0.660
#>  7 2026-05-25    0.474 A                        0.725
#>  8 2026-06-01    0.492 A                        0.780
#>  9 2026-06-08    0.507 A                        0.826
#> 10 2026-06-15    0.518 A                        0.863
#> 11 2026-06-22    0.527 A                        0.893
#> 12 2026-06-29    0.534 A                        0.916
#> 13 2026-07-06    0.539 A                        0.935
#> 14 2026-07-13    0.543 A                        0.949
#> 15 2026-07-20    0.546 A                        0.961
# }