Expected Value of Information for genomic surveillance

Quantifies the marginal decision value of sequencing additional samples from each region or stratum, based on the current posterior uncertainty of variant frequency estimates. The EVOI captures how much the expected estimation error decreases per additional sequence, enabling cost-effective resource allocation.

Usage

surveillance_value(
  fit,
  n_current = 100L,
  n_additional = seq(10L, 200L, by = 10L),
  target_lineage = NULL
)

Arguments

fit

An lfq_fit object from fit_model.

n_current

Integer vector of current sample sizes per stratum. If a single integer, assumed equal across all lineages.

n_additional

Integer vector of candidate additional sample sizes to evaluate. Default seq(10, 200, by = 10).

target_lineage

Optional character; compute EVOI specifically for this lineage. Default NULL evaluates across all non-pivot lineages.

Value

An evoi S3 class with components:

values

Tibble with n_additional, evoi, marginal_evoi columns.

current_uncertainty

Numeric; current estimation variance (sum of growth rate SEs squared).

target_lineage

Character or NULL.

Details

The EVOI is computed as the expected reduction in mean squared estimation error for lineage frequency when \(n\) additional sequences are observed. Under the multinomial likelihood with Gaussian approximation to the posterior, the variance of the frequency estimate scales as \(p(1-p) / n\), and additional samples reduce this by the factor \(n_0 / (n_0 + n_{\text{add}})\).

The marginal EVOI (the value of one additional sequence) is the derivative of the EVOI curve. It decreases monotonically with sample size, exhibiting the diminishing returns characteristic of information-theoretic quantities.

See also

adaptive_design for dynamic allocation, sequencing_power for static sample size planning.

Examples

# \donttest{
sim <- simulate_dynamics(n_lineages = 3,
  advantages = c("A" = 1.3, "B" = 0.9),
  n_timepoints = 15, seed = 1)
fit <- fit_model(sim, engine = "mlr")
ev <- surveillance_value(fit, n_current = 500)
ev
#> 
#> ── Expected Value of Information 
#> Current estimation variance: 0.00032
#> 50% variance reduction at ~80 additional sequences
# }