Include isolates of unknown species in grouped antibiograms and in wisca(), which were silently excluded with the warning 'invalid microorganism code, NA generated'

NEWS.md

@@ -110,6 +110,7 @@ Planned as v3.1.0, mid October 2026.
 * Antibiograms (`antibiogram()` and `wisca()`)
   * `wisca()` now follows Bielicki *et al.* (2016) more strictly by using $\text{Beta}(1, 9999)$ for intrinsically resistant pairs, forcing near-zero susceptibility regardless of observed data (based on EUCAST Expected Resistant Phenotypes)
   * Fixed a bug where both functions failed without the `antimicrobials` argument if `tidyselect` was not available, e.g., for binary installations without `dplyr`
+  * Isolates of unknown species are now included in grouped antibiograms and in `wisca()`, as they already were in traditional antibiograms. They were previously excluded silently, with the warning "invalid microorganism code, NA generated". This can change WISCA coverage estimates.
 * Resistance and susceptibility calculation
   * New argument `guideline` (default: the `AMR_guideline` option, otherwise EUCAST) in `susceptibility()`, `resistance()`, `count_df()`, `proportion_df()`, `sir_df()`, `ggplot_sir()`, `geom_sir()`, `labels_sir_count()`, `antibiogram()`, `wisca()`, and `bug_drug_combinations()` to interpret the 'I' category correctly per guideline. Functions that return results per category also accept `guideline = NULL` to keep S, I, and R separate, while functions that return a single proportion or count give an informative error.
   * When EUCAST is assumed because neither the `guideline` argument nor the `AMR_guideline` option is set, a message explains how to change this, once per session and not in R Markdown or Quarto documents

R/antibiogram.R

@@ -578,7 +578,13 @@ antibiogram.default <- function(x,
       x$`.mo` <- mo_property(x$`.mo`, property = mo_transform, language = language)
     }
   }
-  x$`.mo`[x$`.mo` %in% c(NA, "UNKNOWN")] <- "(??)"
+  # keep unknown microorganisms, so that they are also included in grouped antibiograms and WISCA
+  if (is.mo(x$`.mo`)) {
+    # the 'mo' class only allows valid codes, of which "UNKNOWN" is one
+    x$`.mo`[is.na(x$`.mo`)] <- "UNKNOWN"
+  } else {
+    x$`.mo`[x$`.mo` %in% c(NA, "UNKNOWN")] <- "(??)"
+  }
 
   # get antimicrobials
   ab_trycatch <- tryCatch(colnames(suppressWarnings(x[, antimicrobials, drop = FALSE])), error = function(e) NULL)

index.md

@@ -220,12 +220,11 @@ the full explanation.
 wisca(example_isolates,
       antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
       minimum = 10) # Recommended threshold: >=30
-#> Warning: invalid microorganism code, NA generated
 ```
 
 | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
 |:------------------------|:-------------------------------------|:-------------------------------------|
-| 69.8% (64.5-74.7%)      | 93.6% (92.1-95%)                     | 89.9% (87-92.5%)                     |
+| 70.2% (65-75.4%)        | 93.6% (92-95.1%)                     | 89.9% (87-92.5%)                     |
 
 WISCA supports stratification by any clinical variable, so you can
 generate syndrome-specific or ward-specific coverage estimates:
@@ -235,14 +234,13 @@ wisca(example_isolates,
       antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
       syndromic_group = "ward",
       minimum = 10) # Recommended threshold: >=30
-#> Warning: invalid microorganism code, NA generated
 ```
 
 | Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
 |:----------------|:------------------------|:-------------------------------------|:-------------------------------------|
-| Clinical        | 74.6% (69.1-80.1%)      | 93.7% (92.1-95.1%)                   | 90.3% (87-93.1%)                     |
-| ICU             | 56.9% (48.3-66%)        | 86.7% (83.5-89.8%)                   | 83% (77.9-87.4%)                     |
-| Outpatient      | 57.7% (46.5-69.3%)      | 76.6% (70.5-82.2%)                   | 67.8% (56.9-77.5%)                   |
+| Clinical        | 74.8% (69-80.3%)        | 93.7% (92.1-95.1%)                   | 90.4% (86.8-93.3%)                   |
+| ICU             | 56.7% (48.2-65.4%)      | 86.6% (83.3-89.6%)                   | 83.1% (78.2-87.5%)                   |
+| Outpatient      | 57.6% (46-69%)          | 76.4% (69.8-81.7%)                   | 68% (57.7-77.5%)                     |
 
 **For AMR surveillance**, traditional antibiograms remain the right tool
 for tracking resistance per species over time:

tests/testthat/test-antibiogram.R

@@ -40,6 +40,22 @@ test_that("test-antibiogram.R", {
   expect_identical(get_gen(combine_SI = TRUE, guideline = "CLSI"), gen_eucast)
   expect_identical(get_gen(combine_SI = FALSE, guideline = "EUCAST"), gen_clsi) # identical since example_isolates contains no SDD
 
+  # isolates of unknown species must be included, also in WISCA
+  set.seed(1)
+  wisca_all <- suppressMessages(wisca(example_isolates, antimicrobials = "GEN", simulations = 100, info = FALSE, guideline = "EUCAST"))
+  set.seed(1)
+  wisca_known <- suppressMessages(wisca(example_isolates[example_isolates$mo != "UNKNOWN", ], antimicrobials = "GEN", simulations = 100, info = FALSE, guideline = "EUCAST"))
+  expect_true("UNKNOWN" %in% attributes(wisca_all)$wisca_parameters$mo)
+  expect_false(identical(as.data.frame(wisca_all), as.data.frame(wisca_known)))
+  if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
+    ab_grouped <- tryCatch(
+      example_isolates %>% group_by(ward) %>% antibiogram(antimicrobials = "GEN", minimum = 0, formatting_type = 1, guideline = "EUCAST"),
+      warning = function(w) conditionMessage(w)
+    )
+    expect_inherits(ab_grouped, "antibiogram")
+    expect_true("UNKNOWN" %in% as.character(ab_grouped[[2]]))
+  }
+
   # Traditional antibiogram ----------------------------------------------