Generate a comprehensive surveillance system report

Produces a summary of the current surveillance design's strengths, weaknesses, and recommendations.

Usage

surv_report(design, target_lineage = NULL, target_prevalence = 0.01)

Arguments

design

A surv_design object.

target_lineage

Character or NULL. Lineage to focus on. If NULL, uses the most common non-"Other" lineage.

target_prevalence

Numeric. Assumed prevalence for detection calculations. Default 0.01.

Value

Invisibly returns a named list of computed metrics including n_obs, n_strata, rate_range, gini, effective_n, detection_prob, and mean_bias.

See also

surv_design(), surv_lineage_prevalence(), surv_detection_probability(), surv_optimize_allocation()

Examples

sim <- surv_simulate(n_regions = 3, n_weeks = 10, seed = 1)
d <- surv_design(sim$sequences, ~ region,
                 sim$population[c("region", "seq_rate")], sim$population)
surv_report(d)
#> 
#> ── Surveillance System Report ──────────────────────────────────────────────────
#> 
#> ── Design Structure ──
#> 
#> Total sequences: 759
#> Strata: 3
#> Period: 2024-01-01 to 2024-03-10
#> 
#> ── Sequencing Inequality ──
#> 
#> Rate range: 3.95% to 22.03%
#> Rate ratio: 5.6x
#> Gini coefficient: 0.356
#> ℹ Moderate inequality. Weighting recommended.
#> 
#> ── Estimation Impact ──
#> 
#> Effective sample size: 573 (75.4% of total)
#> Mean |weighted - naive| bias: 2.28 percentage points
#> 
#> ── Detection Power ──
#> 
#> Target: detect BA.5 at 1% prevalence
#> Weekly detection probability: 53.4%
#> Sequences needed for 95% detection: 299