incorporate Bartlett et al (2022)

R/data.R

@@ -99,7 +99,7 @@
 #' - `gbif_parent`\cr GBIF identifier of the parent taxon
 #' - `gbif_renamed_to`\cr GBIF identifier of the currently valid taxon
 #' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
-#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
+#' - `prevalence`\cr Prevalence of the microorganism according to Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}), see [mo_matching_score()] for the full explanation
 #' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, version of `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
 #' @details
 #' Please note that entries are only based on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
@@ -142,7 +142,9 @@
 #'
 #' * `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
 #' 
-#' * Grimont *et al.*. Antigenic Formulae of the Salmonella Serovars, 2007, 9th Edition. WHO Collaborating Centre for Reference and Research on *Salmonella* (WHOCC-SALM).
+#' * Grimont *et al.* (2007). Antigenic Formulae of the Salmonella Serovars, 9th Edition. WHO Collaborating Centre for Reference and Research on *Salmonella* (WHOCC-SALM).
+#' 
+#' * Bartlett *et al.* (2022). **A comprehensive list of bacterial pathogens infecting humans** *Microbiology* 168:001269; \doi{10.1099/mic.0.001269}
 #' @seealso [as.mo()], [mo_property()], [microorganisms.codes], [intrinsic_resistant]
 #' @examples
 #' microorganisms

R/mdro.R

@@ -1289,7 +1289,7 @@ mdro <- function(x = NULL,
     )
     trans_tbl(
       3,
-      which(x$genus == "Clostridium" & x$species == "difficile"),
+      which(x$genus %in% c("Clostridium", "Clostridioides") & x$species == "difficile"),
       c(MTR, VAN),
       "any"
     )
@@ -1390,7 +1390,7 @@ mdro <- function(x = NULL,
     )
     trans_tbl(
       3,
-      which(x$genus == "Clostridium" & x$species == "difficile"),
+      which(x$genus %in% c("Clostridium", "Clostridioides") & x$species == "difficile"),
       c(MTR, VAN, FDX),
       "any"
     )
@@ -1492,7 +1492,7 @@ mdro <- function(x = NULL,
     )
     trans_tbl(
       3,
-      which(x$genus == "Clostridium" & x$species == "difficile"),
+      which(x$genus %in% c("Clostridium", "Clostridioides") & x$species == "difficile"),
       c(MTR, VAN, FDX),
       "any"
     )

R/mo.R

@@ -97,6 +97,7 @@
 #' 7. `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
 #' 8. `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
 #' 9. `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
+#' 10. Bartlett A *et al.* (2022). **A comprehensive list of bacterial pathogens infecting humans** *Microbiology* 168:001269; \doi{10.1099/mic.0.001269}
 #' @export
 #' @return A [character] [vector] with additional class [`mo`]
 #' @seealso [microorganisms] for the [data.frame] that is being used to determine ID's.
@@ -782,7 +783,7 @@ print.mo_uncertainties <- function(x, ...) {
     return(invisible(NULL))
   }
 
-  cat(word_wrap("Matching scores are based on the resemblance between the input and the full taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`.\n\n", add_fn = font_blue))
+  cat(word_wrap("Matching scores are based on the resemblance between the input and the full taxonomic name, and the pathogenicity in humans according to Bartlett ", font_italic("et al."), " (2022). See `?mo_matching_score`.\n\n", add_fn = font_blue))
   if (has_colour()) {
     cat(word_wrap("Colour keys: ",
       font_red_bg(" 0.000-0.499 "),

R/mo_matching_score.R

@@ -33,7 +33,9 @@
 #' @author Dr. Matthijs Berends
 #' @param x Any user input value(s)
 #' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
-#' @note This algorithm was described in: Berends MS *et al.* (2022). **AMR: An R Package for Working with Antimicrobial Resistance Data**. *Journal of Statistical Software*, 104(3), 1-31; \doi{10.18637/jss.v104.i03}.
+#' @note This algorithm was originally described in: Berends MS *et al.* (2022). **AMR: An R Package for Working with Antimicrobial Resistance Data**. *Journal of Statistical Software*, 104(3), 1-31; \doi{10.18637/jss.v104.i03}.
+#' 
+#' Later, the work of Bartlett A *et al.* about bacterial pathogens infecting humans (2022, \doi{10.1099/mic.0.001269}) was incorporated.
 #' @section Matching Score for Microorganisms:
 #' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, is calculated as:
 #'
@@ -48,15 +50,18 @@
 #' * \ifelse{html}{\out{<i>p<sub>n</sub></i> is the human pathogenic prevalence group of <i>n</i>, as described below;}}{p_n is the human pathogenic prevalence group of \eqn{n}, as described below;}
 #' * \ifelse{html}{\out{<i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}}{l_n is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}
 #'
-#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence:
+#' The grouping into human pathogenic prevalence (\eqn{p}) is based on recent work from Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:
+#' 
+#' - **Established**, if a taxonomic species has infected at least three persons in three or more references. These records have `prevalence = 1.0` in the [microorganisms] data set.
+#' - **Putative**, if a taxonomic species has fewer than three known cases. These records have `prevalence = 2.0` in the [microorganisms] data set.
+#' 
+#' Furthermore,
+#' 
+#' - Any *other* bacterial genus, species or subspecies of which the genus is present in the two aforementioned groups, has `prevalence = 2.5` in the [microorganisms] data set. 
+#' - Any *non-bacterial* genus, species or subspecies of which the genus is present in the following list, also has `prevalence = 2.5` in the [microorganisms] data set: `r vector_or(MO_PREVALENT_GENERA, quotes = "*")`.
+#' - All other records have `prevalence = 3.0` in the [microorganisms] data set.
 #'
-#' **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales.
-#'
-#' **Group 2** consists of all microorganisms where the taxonomic phylum is Pseudomonadota (previously named Proteobacteria), Bacillota (previously named Firmicutes), Actinomycetota (previously named Actinobacteria) or Sarcomastigophora, or where the taxonomic genus is `r vector_or(MO_PREVALENT_GENERA, quotes = "*")`.
-#'
-#' **Group 3** consists of all other microorganisms.
-#'
-#' All characters in \eqn{x} and \eqn{n} are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.
+#' When calculating the matching score, all characters in \eqn{x} and \eqn{n} are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.
 #'
 #' All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of *Escherichia coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Escherichia coli"), 3)`}, a highly prevalent microorganism found in humans) and not *Entamoeba coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Entamoeba coli"), 3)`}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
 #' @export
@@ -82,7 +87,7 @@ mo_matching_score <- function(x, n) {
 
   # force a capital letter, so this conversion will not count as a substitution
   substr(x, 1, 1) <- toupper(substr(x, 1, 1))
-
+  
   # n is always a taxonomically valid full name
   if (length(n) == 1) {
     n <- rep(n, length(x))
@@ -90,11 +95,12 @@ mo_matching_score <- function(x, n) {
   if (length(x) == 1) {
     x <- rep(x, length(n))
   }
-
+  
   # length of fullname
   l_n <- nchar(n)
   lev <- double(length = length(x))
   l_n.lev <- double(length = length(x))
+  # get Levenshtein distance
   lev <- unlist(Map(f = function(a, b) {
     as.double(utils::adist(a, b,
       ignore.case = FALSE,
@@ -112,7 +118,7 @@ mo_matching_score <- function(x, n) {
   p_n <- AMR_env$MO_lookup[match(n, AMR_env$MO_lookup$fullname), "prevalence", drop = TRUE]
   # kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
   k_n <- AMR_env$MO_lookup[match(n, AMR_env$MO_lookup$fullname), "kingdom_index", drop = TRUE]
-
+  
   # matching score:
   (l_n - 0.5 * l_n.lev) / (l_n * p_n * k_n)
 }