Optimizing Sequencing Resource Allocation

Why allocation matters

In genomic surveillance, the total number of sequences you can generate each week is fixed by lab capacity and budget. The question is not how many to sequence (phylosamp answers that), but how to distribute a fixed number across regions, institutions, and sample sources.

Poor allocation wastes resources. If you sequence proportionally to submissions (which is what most systems do by default), you over-represent regions that send more samples — which are often the same regions that already have the highest sequencing rates.

The three objectives

survinger supports three optimization objectives:

min_mse: Minimize the mean squared error of lineage prevalence estimates. This is a Neyman-type allocation that gives more sequences to high-variance strata.
max_detection: Maximize the probability of detecting a rare variant. This spreads sequences to maximize geographic coverage.
min_imbalance: Minimize the deviation from population-proportional representation. This ensures each region is fairly represented.

Example

library(survinger)
data(sarscov2_surveillance)

design <- surv_design(
  data = sarscov2_surveillance$sequences,
  strata = ~ region,
  sequencing_rate = sarscov2_surveillance$population[c("region", "seq_rate")],
  population = sarscov2_surveillance$population,
  source_type = "source_type"
)

Optimize for minimum MSE

alloc_mse <- surv_optimize_allocation(design, "min_mse", total_capacity = 500)
print(alloc_mse)
#> ── Optimal Sequencing Allocation ───────────────────────────────────────────────
#> Objective: min_mse
#> Total capacity: 500 sequences
#> Strata: 5
#> 
#> # A tibble: 5 × 3
#>   region   n_allocated proportion
#>   <chr>          <int>      <dbl>
#> 1 Region_A         130      0.26 
#> 2 Region_B          42      0.084
#> 3 Region_C          44      0.088
#> 4 Region_D         166      0.332
#> 5 Region_E         118      0.236
plot(alloc_mse)

Compare all strategies

comparison <- surv_compare_allocations(design, total_capacity = 500)
print(comparison)
#> # A tibble: 5 × 4
#>   strategy      total_mse detection_prob  imbalance
#>   <chr>             <dbl>          <dbl>      <dbl>
#> 1 equal          0.000569          0.993 0.0486    
#> 2 proportional   0.000460          0.993 0.00000482
#> 3 min_mse        0.000459          0.993 0.0000292 
#> 4 max_detection  0.000560          0.993 0.0456    
#> 5 min_imbalance  0.000460          0.993 0.00000482

The table shows the trade-off: minimizing MSE may increase imbalance, while proportional allocation sacrifices detection power.

With minimum coverage constraints

alloc_floor <- surv_optimize_allocation(
  design, "min_mse", total_capacity = 500, min_per_stratum = 20
)
print(alloc_floor)
#> ── Optimal Sequencing Allocation ───────────────────────────────────────────────
#> Objective: min_mse
#> Total capacity: 500 sequences
#> Strata: 5
#> 
#> # A tibble: 5 × 3
#>   region   n_allocated proportion
#>   <chr>          <int>      <dbl>
#> 1 Region_A         130      0.26 
#> 2 Region_B          42      0.084
#> 3 Region_C          44      0.088
#> 4 Region_D         166      0.332
#> 5 Region_E         118      0.236

Setting min_per_stratum = 20 ensures every region gets at least 20 sequences, preventing any region from being invisible.

Choosing an objective

Use min_mse when your primary goal is accurate prevalence tracking.
Use max_detection when hunting for rare emerging variants.
Use min_imbalance when equity and representativeness are priorities.

In practice, reviewing the surv_compare_allocations() output helps stakeholders understand the trade-offs and choose based on their mandate.