(v2.1.1.9082) algorithm updates

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9081
-Date: 2024-09-29
+Version: 2.1.1.9082
+Date: 2024-09-30
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9081
+# AMR 2.1.1.9082
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
 
@@ -20,7 +20,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
   * MycoBank has now been integrated as the primary taxonomic source for fungi. The `microorganisms` data set has been enriched with new columns (`mycobank`, `mycobank_parent`, and `mycobank_renamed_to`) that provide detailed information for fungal species.
   * A remarkable addition of over 20,000 new fungal records
   * New function `mo_mycobank()` to retrieve the MycoBank record number, analogous to existing functions such as `mo_lpsn()` and `mo_gbif()`.
-  * The `as.mo()` function and all `mo_*()` functions now includes an `only_fungi` argument, allowing users to restrict results solely to fungal species. This ensures fungi are prioritised over bacteria during microorganism identification. This can also be set globally with the new `AMR_only_fungi` option.
+  * The `as.mo()` function and all `mo_*()` functions now include an `only_fungi` argument, allowing users to restrict results solely to fungal species. This ensures fungi are prioritised over bacteria during microorganism identification. This can also be set globally with the new `AMR_only_fungi` option.
   * Also updated other kingdoms, welcoming a total of 2,149 new records from 2023 and 927 from 2024.
 * **Updated clinical breakpoints**
   * EUCAST 2024 and CLSI 2024 are now supported, by adding all of their over 4,000 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2024 is now the new default guideline for all MIC and disk diffusion interpretations.
@@ -63,10 +63,11 @@ This package now supports not only tools for AMR data analysis in clinical setti
 * Improved overall algorithm of `as.mo()` for better performance and accuracy. Specifically, more weight is given to genus and species combinations in cases where the subspecies is miswritten, so that the result will be the correct genus and species.
 * Intermediate log2 levels used for MIC plotting are now more common values instead of following a strict dilution range
 * Fixed a bug for when `antibiogram()` returns an empty data set
+* Updated the prevalence calculation to include genera from the World Health Organization's (WHO) Priority Pathogen List
 
 ## Other
 * Greatly updated and expanded documentation
-* Added Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input
+* Added Larisse Bolton, Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input
 * Stopped support for SAS (`.xpt`) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.
 
 ## Older Versions

R/aa_helper_functions.R

@@ -1547,6 +1547,7 @@ add_MO_lookup_to_AMR_env <- function() {
     MO_lookup[which(MO_lookup$kingdom == "Bacteria" | MO_lookup$mo == "UNKNOWN"), "kingdom_index"] <- 1
     MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 1.25
     MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 1.5
+    MO_lookup[which(MO_lookup$kingdom == "Chromista"), "kingdom_index"] <- 1.75
     MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 2
     # all the rest
     MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 3

R/first_isolate.R

@@ -670,6 +670,16 @@ duplicated_antibiogram <- function(antibiogram, points_threshold, ignore_I, type
     # fast return, only 1 isolate
     return(FALSE)
   }
+  stop("Check R/first_isolate.R -> duplicated_antibiogram()")
+  # first sort on data availability - count the dots and order that ascending
+  number_dots <- vapply(FUN.VALUE = integer(1),
+                        antibiogram,
+                        function(x) sum(strsplit(x, "", fixed = TRUE)[[1]] == "."),
+                        USE.NAMES = FALSE)
+  new_order <- order(number_dots, antibiogram)
+  antibiogram.bak <- antibiogram
+  antibiogram <- antibiogram[new_order]
+  
   out <- rep(NA, length(antibiogram))
   out[1] <- FALSE
   out[2] <- antimicrobials_equal(antibiogram[1], antibiogram[2],
@@ -680,11 +690,6 @@ duplicated_antibiogram <- function(antibiogram, points_threshold, ignore_I, type
     return(out)
   }
   
-  # sort after the second one (since we already determined AB equality of the first two)
-  original_sort <- c(1, 2, rank(antibiogram[3:length(antibiogram)]) + 2)
-  antibiogram.bak <- antibiogram
-  antibiogram <- c(antibiogram[1:2], sort(antibiogram[3:length(antibiogram)]))
-  
   # we can skip the duplicates - they are never unique antibiograms of course
   duplicates <- duplicated(antibiogram)
   out[3:length(out)][duplicates[3:length(out)] == TRUE] <- TRUE
@@ -703,7 +708,7 @@ duplicated_antibiogram <- function(antibiogram, points_threshold, ignore_I, type
                                               type = type)))
   }
   
-  out <- out[original_sort]
+  out <- out[order(new_order)]
   # rerun duplicated again
   duplicates <- duplicated(antibiogram.bak)
   out[duplicates == TRUE] <- TRUE

R/mo.R

@@ -1081,6 +1081,9 @@ convert_colloquial_input <- function(x) {
   out[x %like_case% "(^| )yeast?"] <- "F_YEAST"
   out[x %like_case% "(^| )fung(us|i)"] <- "F_FUNGUS"
   
+  # protozoa
+  out[x %like_case% "protozo"] <- "P_PROTOZOAN" # to hit it with most languages, and "protozo" does not occur in the microorganisms data set for anything else
+  
   # trivial names known to the field
   out[x %like_case% "meningo[ck]o[ck]"] <- "B_NESSR_MNNG"
   out[x %like_case% "gono[ck]o[ck]"] <- "B_NESSR_GNRR"

R/mo_matching_score.R

@@ -30,12 +30,11 @@
 #' Calculate the Matching Score for Microorganisms
 #'
 #' This algorithm is used by [as.mo()] and all the [`mo_*`][mo_property()] functions to determine the most probable match of taxonomic records based on user input.
-#' @author Dr. Matthijs Berends, 2018
 #' @param x Any user input value(s)
 #' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
-#' @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}.
+#' @note This algorithm was originally developed in 2018 and subsequently 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.
+#' Later, the work of Bartlett A *et al.* about bacterial pathogens infecting humans (2022, \doi{10.1099/mic.0.001269}) was incorporated, and optimalisations to the algorithm were made.
 #' @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:
 #'
@@ -50,16 +49,17 @@
 #' * \eqn{l_n} is the length of \eqn{n};
 #' * \eqn{lev} is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance) (counting any insertion as 1, and any deletion or substitution as 2) that is needed to change \eqn{x} into \eqn{n};
 #' * \eqn{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
-#' * \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Archaea = 2, others = 3.
+#' * \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.
 #'
 #' 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;
+#' - **Established**, if a taxonomic species has infected at least three persons in three or more references. These records have `prevalence = 1.15` in the [microorganisms] data set;
 #' - **Putative**, if a taxonomic species has fewer than three known cases. These records have `prevalence = 1.25` in the [microorganisms] data set.
 #'
 #' Furthermore,
 #'
-#' - Any genus present in the **established** list also has `prevalence = 1.0` in the [microorganisms] data set;
+#' - Genera from the World Health Organization's (WHO) Priority Pathogen List have `prevalence = 1.0` in the [microorganisms] data set;
+#' - Any genus present in the **established** list also has `prevalence = 1.15` in the [microorganisms] data set;
 #' - Any other genus present in the **putative** list has `prevalence = 1.25` in the [microorganisms] data set;
 #' - Any other species or subspecies of which the genus is present in the two aforementioned groups, has `prevalence = 1.5` in the [microorganisms] data set;
 #' - Any *non-bacterial* genus, species or subspecies of which the genus is present in the following list, has `prevalence = 1.25` in the [microorganisms] data set: `r vector_or(MO_RELEVANT_GENERA, quotes = "*")`;

R/mo_property.R

@@ -442,8 +442,8 @@ mo_pathogenicity <- function(x, language = get_AMR_locale(), keep_synonyms = get
   kngd <- AMR_env$MO_lookup$kingdom[match(x.mo, AMR_env$MO_lookup$mo)]
   rank <- AMR_env$MO_lookup$rank[match(x.mo, AMR_env$MO_lookup$mo)]
 
-  out <- factor(case_when_AMR(prev == 1 & kngd == "Bacteria" & rank != "genus" ~ "Pathogenic",
-                              (prev < 2 & kngd == "Fungi") ~ "Potentially pathogenic",
+  out <- factor(case_when_AMR(prev <= 1.15 & kngd == "Bacteria" & rank != "genus" ~ "Pathogenic",
+                              prev < 2 & kngd == "Fungi" ~ "Potentially pathogenic",
                               prev == 2 & kngd == "Bacteria" ~ "Non-pathogenic",
                               kngd == "Bacteria" ~ "Potentially pathogenic",
                               TRUE ~ "Unknown"),

data-raw/_pre_commit_checks.R

@@ -158,6 +158,32 @@ pre_commit_lst$MO_STREP_ABCG <- AMR::microorganisms$mo[which(AMR::microorganisms
     "group a", "group b", "group c", "group g"
   ))]
 pre_commit_lst$MO_LANCEFIELD <- AMR::microorganisms$mo[which(AMR::microorganisms$mo %like% "^(B_STRPT_PYGN(_|$)|B_STRPT_AGLC(_|$)|B_STRPT_(DYSG|EQUI)(_|$)|B_STRPT_ANGN(_|$)|B_STRPT_(DYSG|CANS)(_|$)|B_STRPT_SNGN(_|$)|B_STRPT_SLVR(_|$))")]
+pre_commit_lst$MO_WHO_PRIORITY_GENERA <- c(
+  # World Health Organization's (WHO) Priority Pathogen List
+  "Acinetobacter",
+  "Aspergillus",
+  "Blastomyces",
+  "Campylobacter",
+  "Candida",
+  "Clostridioides",
+  "Coccidioides",
+  "Cryptococcus",
+  "Enterococcus",
+  "Fusarium",
+  "Haemophilus",
+  "Helicobacter",
+  "Histoplasma",
+  "Klebsiella",
+  "Mycobacterium",
+  "Neisseria",
+  "Paracoccidioides",
+  "Pneumocystis",
+  "Pseudomonas",
+  "Salmonella",
+  "Shigella",
+  "Staphylococcus",
+  "Streptococcus"
+)
 pre_commit_lst$MO_RELEVANT_GENERA <- c(
   "Absidia",
   "Acanthamoeba",

data-raw/microorganisms.md5

@@ -1 +1 @@
-f1cbe414851c1eee08ff5440a47af76c
+5b5544d28deade33092925a6758277c4

data-raw/reproduction_of_microorganisms.R

@@ -455,7 +455,7 @@ sort(table(taxonomy_lpsn$status))
 
 # Read MycoBank data ------------------------------------------------------------------------------
 
-taxonomy_mycobank <- readxl::read_excel(file_mycobank, guess_max = 1e5)
+taxonomy_mycobank <- read_excel(file_mycobank, guess_max = 1e5)
 taxonomy_mycobank.bak <- taxonomy_mycobank
 
 taxonomy_mycobank <- taxonomy_mycobank %>%
@@ -1224,10 +1224,12 @@ nonbacterial_genera <- nonbacterial_genera[nonbacterial_genera %unlike% "unknown
 # see https://doi.org/10.1099/mic.0.001269
 taxonomy <- taxonomy %>%
   mutate(prevalence = case_when(
+    # genera of the pathogens mentioned in the World Health Organization's (WHO) Priority Pathogen List
+    genus %in% MO_WHO_PRIORITY_GENERA ~ 1.0,
     # 'established' means 'have infected at least three persons in three or more references'
-    paste(genus, species) %in% established & rank %in% c("species", "subspecies") ~ 1.0,
+    paste(genus, species) %in% established & rank %in% c("species", "subspecies") ~ 1.15,
     # other genera in the 'established' group
-    genus %in% established_genera & rank == "genus" ~ 1.0,
+    genus %in% established_genera & rank == "genus" ~ 1.15,
     
     # 'putative' means 'fewer than three known cases'
     paste(genus, species) %in% putative & rank %in% c("species", "subspecies") ~ 1.25,
@@ -2148,7 +2150,7 @@ microorganisms <- taxonomy
 
 # set class <mo>
 class(microorganisms$mo) <- c("mo", "character")
-microorganisms <- microorganisms %>% arrange(fullname)
+microorganisms <- microorganisms %>% arrange(fullname) %>% df_remove_nonASCII()
 usethis::use_data(microorganisms, overwrite = TRUE, version = 2, compress = "xz")
 rm(microorganisms)
 

data-raw/translations.tsv

@@ -8,6 +8,7 @@ unknown Gram-negatives	TRUE	TRUE	FALSE	TRUE	不明革兰氏阴性菌	neznámé g
 unknown Gram-positives	TRUE	TRUE	FALSE	TRUE	不明革兰氏阳性菌	neznámé grampozitivní	ukendte Gram-positive	onbekende Gram-positieven	tuntemattomat grampositiiviset	Gram positifs inconnus	unbekannte Grampositiven	άγνωστοι θετικοί κατά Gram	Gram positivi sconosciuti	未知のグラム陽性菌	ukjent Gram-positive	Nieznane bakterie Gram-dodatnie	Gram positivos desconhecidos	Gram-pozitive necunoscute	неизвестные грамположительные	Gram positivos desconocidos	okända Gram-positiva	bilinmeyen Gram-pozitifler	невідомі грампозитивні
 unknown anaerobic Gram-negatives	TRUE	TRUE	FALSE	TRUE			ukendte anaerobe Gram-negative	onbekende anaerobe Gram-negatieven			unbekannte anaerobe Gramnegativen												
 unknown anaerobic Gram-positives	TRUE	TRUE	FALSE	TRUE			ukendte anaerobe Gram-positive	onbekende anaerobe Gram-positieven			unbekannte anaerobe Grampositiven												
+unknown protozoan	TRUE	TRUE	FALSE	TRUE	未知原生动物	neznámý prvok	ukendt protozo	onbekend protozoön	tuntematon alkueläin	protozoaire inconnu	unbekanntes Protozoon	άγνωστο πρωτόζωο	protozoo sconosciuto	未知の原生動物	ukjent protozo	nieznany pierwotniak	protozoário desconhecido	protozoar necunoscut	неизвестное простейшее	protozoo desconocido	okänd protozo	bilinmeyen protozoa	невідоме найпростіше
 unknown fungus	TRUE	TRUE	FALSE	TRUE	未知真菌	neznámé houby	ukendt svamp	onbekende schimmel	tuntematon sieni	champignon inconnu	unbekannter Pilze	άγνωστος μύκητας	fungo sconosciuto	未知真菌	ukjent sopp	Nieznany grzyb	fungo desconhecido	ciuperci necunoscute	неизвестный грибок	hongo desconocido	Okänd svamp	bilinmeyen mantar	невідомий гриб
 unknown yeast	TRUE	TRUE	FALSE	TRUE	未知酵母菌	neznámé kvasinky	ukendt gær	onbekende gist	tuntematon hiiva	levure inconnue	unbekannte Hefe	άγνωστος ζυμομύκητας	lievito sconosciuto	未知酵母	ukjent gjær	Nieznany drożdżak	levedura desconhecida	drojdie necunoscută	неизвестные дрожжи	levadura desconocida	Okänd jäst	bilinmeyen maya	невідомі дріжджі
 unknown name	TRUE	TRUE	FALSE	TRUE	不明名称	neznámý název	ukendt navn	onbekende naam	tuntematon nimi	nom inconnu	unbekannte Name	άγνωστο όνομα	nome sconosciuto	名称未知	ukjent navn	nieznana nazwa	nome desconhecido	nume necunoscut	неизвестное название	nombre desconocido	okänt namn	bilinmeyen isim	невідома назва

inst/tinytest/test-mo_property.R

@@ -94,7 +94,7 @@ expect_equal(mo_synonyms("Escherichia coli"), NULL)
 expect_true(length(mo_synonyms("Candida albicans")) > 1)
 expect_inherits(mo_synonyms(c("Candida albicans", "Escherichia coli")), "list")
 expect_equal(names(mo_info("Escherichia coli")), c(
-  "mo",
+  "mo", "rank",
   "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies",
   "status", "synonyms", "gramstain", "oxygen_tolerance",
   "url", "ref", "snomed", "lpsn", "mycobank", "gbif", "group_members"

man/as.mo.Rd

@@ -186,18 +186,19 @@ where:
 \item \eqn{l_n} is the length of \eqn{n};
 \item \eqn{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function} (counting any insertion as 1, and any deletion or substitution as 2) that is needed to change \eqn{x} into \eqn{n};
 \item \eqn{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
-\item \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Archaea = 2, others = 3.
+\item \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.
 }
 
 The grouping into human pathogenic prevalence \eqn{p} is based on recent work from Bartlett \emph{et al.} (2022, \doi{10.1099/mic.0.001269}) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:
 \itemize{
-\item \strong{Established}, if a taxonomic species has infected at least three persons in three or more references. These records have \code{prevalence = 1.0} in the \link{microorganisms} data set;
+\item \strong{Established}, if a taxonomic species has infected at least three persons in three or more references. These records have \code{prevalence = 1.15} in the \link{microorganisms} data set;
 \item \strong{Putative}, if a taxonomic species has fewer than three known cases. These records have \code{prevalence = 1.25} in the \link{microorganisms} data set.
 }
 
 Furthermore,
 \itemize{
-\item Any genus present in the \strong{established} list also has \code{prevalence = 1.0} in the \link{microorganisms} data set;
+\item Genera from the World Health Organization's (WHO) Priority Pathogen List have \code{prevalence = 1.0} in the \link{microorganisms} data set;
+\item Any genus present in the \strong{established} list also has \code{prevalence = 1.15} in the \link{microorganisms} data set;
 \item Any other genus present in the \strong{putative} list has \code{prevalence = 1.25} in the \link{microorganisms} data set;
 \item Any other species or subspecies of which the genus is present in the two aforementioned groups, has \code{prevalence = 1.5} in the \link{microorganisms} data set;
 \item Any \emph{non-bacterial} genus, species or subspecies of which the genus is present in the following list, has \code{prevalence = 1.25} in the \link{microorganisms} data set: \emph{Absidia}, \emph{Acanthamoeba}, \emph{Acremonium}, \emph{Actinomucor}, \emph{Aedes}, \emph{Alternaria}, \emph{Amoeba}, \emph{Ancylostoma}, \emph{Angiostrongylus}, \emph{Anisakis}, \emph{Anopheles}, \emph{Apophysomyces}, \emph{Arthroderma}, \emph{Aspergillus}, \emph{Aureobasidium}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Bipolaris}, \emph{Blastobotrys}, \emph{Blastocystis}, \emph{Blastomyces}, \emph{Candida}, \emph{Capillaria}, \emph{Chaetomium}, \emph{Chilomastix}, \emph{Chrysonilia}, \emph{Chrysosporium}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Clavispora}, \emph{Coccidioides}, \emph{Cokeromyces}, \emph{Conidiobolus}, \emph{Coniochaeta}, \emph{Contracaecum}, \emph{Cordylobia}, \emph{Cryptococcus}, \emph{Cryptosporidium}, \emph{Cunninghamella}, \emph{Curvularia}, \emph{Cyberlindnera}, \emph{Debaryozyma}, \emph{Demodex}, \emph{Dermatobia}, \emph{Dientamoeba}, \emph{Diphyllobothrium}, \emph{Dirofilaria}, \emph{Echinostoma}, \emph{Entamoeba}, \emph{Enterobius}, \emph{Epidermophyton}, \emph{Exidia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Fasciola}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Geotrichum}, \emph{Giardia}, \emph{Graphium}, \emph{Haloarcula}, \emph{Halobacterium}, \emph{Halococcus}, \emph{Hansenula}, \emph{Hendersonula}, \emph{Heterophyes}, \emph{Histomonas}, \emph{Histoplasma}, \emph{Hortaea}, \emph{Hymenolepis}, \emph{Hypomyces}, \emph{Hysterothylacium}, \emph{Kloeckera}, \emph{Kluyveromyces}, \emph{Kodamaea}, \emph{Lacazia}, \emph{Leishmania}, \emph{Lichtheimia}, \emph{Lodderomyces}, \emph{Lomentospora}, \emph{Madurella}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Metagonimus}, \emph{Meyerozyma}, \emph{Microsporidium}, \emph{Microsporum}, \emph{Millerozyma}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Nannizzia}, \emph{Necator}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oesophagostomum}, \emph{Oidiodendron}, \emph{Opisthorchis}, \emph{Paecilomyces}, \emph{Paracoccidioides}, \emph{Pediculus}, \emph{Penicillium}, \emph{Phaeoacremonium}, \emph{Phaeomoniella}, \emph{Phialophora}, \emph{Phlebotomus}, \emph{Phoma}, \emph{Pichia}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Pneumocystis}, \emph{Pseudallescheria}, \emph{Pseudoscopulariopsis}, \emph{Pseudoterranova}, \emph{Pulex}, \emph{Purpureocillium}, \emph{Quambalaria}, \emph{Rhinocladiella}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Saccharomyces}, \emph{Saksenaea}, \emph{Saprochaete}, \emph{Sarcoptes}, \emph{Scedosporium}, \emph{Schistosoma}, \emph{Schizosaccharomyces}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium}, \emph{Spirometra}, \emph{Sporobolomyces}, \emph{Sporopachydermia}, \emph{Sporothrix}, \emph{Sporotrichum}, \emph{Stachybotrys}, \emph{Strongyloides}, \emph{Syncephalastrum}, \emph{Syngamus}, \emph{Taenia}, \emph{Talaromyces}, \emph{Teleomorph}, \emph{Toxocara}, \emph{Trichinella}, \emph{Trichobilharzia}, \emph{Trichoderma}, \emph{Trichomonas}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Trichostrongylus}, \emph{Trichuris}, \emph{Tritirachium}, \emph{Trombicula}, \emph{Trypanosoma}, \emph{Tunga}, \emph{Ulocladium}, \emph{Ustilago}, \emph{Verticillium}, \emph{Wallemia}, \emph{Wangiella}, \emph{Wickerhamomyces}, \emph{Wuchereria}, \emph{Yarrowia}, or \emph{Zygosaccharomyces};
@@ -206,7 +207,7 @@ Furthermore,
 
 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., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.688}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.381}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
+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., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.598}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.381}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
 }
 
 \section{Reference Data Publicly Available}{

man/mo_matching_score.Rd

@@ -15,9 +15,9 @@ mo_matching_score(x, n)
 This algorithm is used by \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions to determine the most probable match of taxonomic records based on user input.
 }
 \note{
-This algorithm was originally described in: Berends MS \emph{et al.} (2022). \strong{AMR: An R Package for Working with Antimicrobial Resistance Data}. \emph{Journal of Statistical Software}, 104(3), 1-31; \doi{10.18637/jss.v104.i03}.
+This algorithm was originally developed in 2018 and subsequently described in: Berends MS \emph{et al.} (2022). \strong{AMR: An R Package for Working with Antimicrobial Resistance Data}. \emph{Journal of Statistical Software}, 104(3), 1-31; \doi{10.18637/jss.v104.i03}.
 
-Later, the work of Bartlett A \emph{et al.} about bacterial pathogens infecting humans (2022, \doi{10.1099/mic.0.001269}) was incorporated.
+Later, the work of Bartlett A \emph{et al.} about bacterial pathogens infecting humans (2022, \doi{10.1099/mic.0.001269}) was incorporated, and optimalisations to the algorithm were made.
 }
 \section{Matching Score for Microorganisms}{
 
@@ -34,18 +34,19 @@ where:
 \item \eqn{l_n} is the length of \eqn{n};
 \item \eqn{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function} (counting any insertion as 1, and any deletion or substitution as 2) that is needed to change \eqn{x} into \eqn{n};
 \item \eqn{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
-\item \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Archaea = 2, others = 3.
+\item \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.
 }
 
 The grouping into human pathogenic prevalence \eqn{p} is based on recent work from Bartlett \emph{et al.} (2022, \doi{10.1099/mic.0.001269}) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:
 \itemize{
-\item \strong{Established}, if a taxonomic species has infected at least three persons in three or more references. These records have \code{prevalence = 1.0} in the \link{microorganisms} data set;
+\item \strong{Established}, if a taxonomic species has infected at least three persons in three or more references. These records have \code{prevalence = 1.15} in the \link{microorganisms} data set;
 \item \strong{Putative}, if a taxonomic species has fewer than three known cases. These records have \code{prevalence = 1.25} in the \link{microorganisms} data set.
 }
 
 Furthermore,
 \itemize{
-\item Any genus present in the \strong{established} list also has \code{prevalence = 1.0} in the \link{microorganisms} data set;
+\item Genera from the World Health Organization's (WHO) Priority Pathogen List have \code{prevalence = 1.0} in the \link{microorganisms} data set;
+\item Any genus present in the \strong{established} list also has \code{prevalence = 1.15} in the \link{microorganisms} data set;
 \item Any other genus present in the \strong{putative} list has \code{prevalence = 1.25} in the \link{microorganisms} data set;
 \item Any other species or subspecies of which the genus is present in the two aforementioned groups, has \code{prevalence = 1.5} in the \link{microorganisms} data set;
 \item Any \emph{non-bacterial} genus, species or subspecies of which the genus is present in the following list, has \code{prevalence = 1.25} in the \link{microorganisms} data set: \emph{Absidia}, \emph{Acanthamoeba}, \emph{Acremonium}, \emph{Actinomucor}, \emph{Aedes}, \emph{Alternaria}, \emph{Amoeba}, \emph{Ancylostoma}, \emph{Angiostrongylus}, \emph{Anisakis}, \emph{Anopheles}, \emph{Apophysomyces}, \emph{Arthroderma}, \emph{Aspergillus}, \emph{Aureobasidium}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Bipolaris}, \emph{Blastobotrys}, \emph{Blastocystis}, \emph{Blastomyces}, \emph{Candida}, \emph{Capillaria}, \emph{Chaetomium}, \emph{Chilomastix}, \emph{Chrysonilia}, \emph{Chrysosporium}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Clavispora}, \emph{Coccidioides}, \emph{Cokeromyces}, \emph{Conidiobolus}, \emph{Coniochaeta}, \emph{Contracaecum}, \emph{Cordylobia}, \emph{Cryptococcus}, \emph{Cryptosporidium}, \emph{Cunninghamella}, \emph{Curvularia}, \emph{Cyberlindnera}, \emph{Debaryozyma}, \emph{Demodex}, \emph{Dermatobia}, \emph{Dientamoeba}, \emph{Diphyllobothrium}, \emph{Dirofilaria}, \emph{Echinostoma}, \emph{Entamoeba}, \emph{Enterobius}, \emph{Epidermophyton}, \emph{Exidia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Fasciola}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Geotrichum}, \emph{Giardia}, \emph{Graphium}, \emph{Haloarcula}, \emph{Halobacterium}, \emph{Halococcus}, \emph{Hansenula}, \emph{Hendersonula}, \emph{Heterophyes}, \emph{Histomonas}, \emph{Histoplasma}, \emph{Hortaea}, \emph{Hymenolepis}, \emph{Hypomyces}, \emph{Hysterothylacium}, \emph{Kloeckera}, \emph{Kluyveromyces}, \emph{Kodamaea}, \emph{Lacazia}, \emph{Leishmania}, \emph{Lichtheimia}, \emph{Lodderomyces}, \emph{Lomentospora}, \emph{Madurella}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Metagonimus}, \emph{Meyerozyma}, \emph{Microsporidium}, \emph{Microsporum}, \emph{Millerozyma}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Nannizzia}, \emph{Necator}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oesophagostomum}, \emph{Oidiodendron}, \emph{Opisthorchis}, \emph{Paecilomyces}, \emph{Paracoccidioides}, \emph{Pediculus}, \emph{Penicillium}, \emph{Phaeoacremonium}, \emph{Phaeomoniella}, \emph{Phialophora}, \emph{Phlebotomus}, \emph{Phoma}, \emph{Pichia}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Pneumocystis}, \emph{Pseudallescheria}, \emph{Pseudoscopulariopsis}, \emph{Pseudoterranova}, \emph{Pulex}, \emph{Purpureocillium}, \emph{Quambalaria}, \emph{Rhinocladiella}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Saccharomyces}, \emph{Saksenaea}, \emph{Saprochaete}, \emph{Sarcoptes}, \emph{Scedosporium}, \emph{Schistosoma}, \emph{Schizosaccharomyces}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium}, \emph{Spirometra}, \emph{Sporobolomyces}, \emph{Sporopachydermia}, \emph{Sporothrix}, \emph{Sporotrichum}, \emph{Stachybotrys}, \emph{Strongyloides}, \emph{Syncephalastrum}, \emph{Syngamus}, \emph{Taenia}, \emph{Talaromyces}, \emph{Teleomorph}, \emph{Toxocara}, \emph{Trichinella}, \emph{Trichobilharzia}, \emph{Trichoderma}, \emph{Trichomonas}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Trichostrongylus}, \emph{Trichuris}, \emph{Tritirachium}, \emph{Trombicula}, \emph{Trypanosoma}, \emph{Tunga}, \emph{Ulocladium}, \emph{Ustilago}, \emph{Verticillium}, \emph{Wallemia}, \emph{Wangiella}, \emph{Wickerhamomyces}, \emph{Wuchereria}, \emph{Yarrowia}, or \emph{Zygosaccharomyces};
@@ -54,7 +55,7 @@ Furthermore,
 
 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., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.688}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.381}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
+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., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.598}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.381}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
 }
 
 \section{Reference Data Publicly Available}{
@@ -73,6 +74,3 @@ mo_matching_score(
   n = c("Escherichia coli", "Entamoeba coli")
 )
 }
-\author{
-Dr. Matthijs Berends, 2018
-}