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pratikunterwegspratikunterwegs
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Add more exampled data
1 parent 3670db4 commit cb96800

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‎R/example_data.R‎

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#'
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#' @source Prepared by the VIMC secretariat.
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"eg_burden_template"
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#' Example of scenario coverage data
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#'
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#' @format ## `eg_coverage`
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#' A data frame with 11 rows and 19 columns.
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#' \describe{
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#' \item{scenario_type}{Scenario type name.}
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#' \item{scenario_type_description}{Scenario type description string.}
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#' \item{scenario}{Scenario name string.}
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#' \item{scenario_description}{Scenario description string.}
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#' \item{coverage_set}{Coverage set string.}
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#' \item{gavi_support_level}{String for whether GAVI supported the scenario.}
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#' \item{source_from}{String identifier for the source.}
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#' \item{disease}{Infection identifier.}
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#' \item{vaccine}{Vaccine identifier.}
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#' \item{activity_type}{Vaccination activity identifier.}
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#' \item{year}{Year}
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#' \item{country}{Country name in short format; this is a placeholder name.}
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#' \item{age_from}{Age limit lower limit.}
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#' \item{age_to}{Age limit upper limit.}
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#' \item{age_range_verbatim}{Description of age range.}
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#' \item{target}{Target for vaccination.}
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#' \item{coverage}{Proportional coverage.}
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#' \item{gender}{Sex to which data applies, may be "Male", "Female", or
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#' "Both".}
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#' \item{proportion_risk}{Proportional risk value.}
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#' }
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#'
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#' @keywords data
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#'
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#' @source Prepared by the VIMC secretariat.
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"eg_coverage"
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#' Example of FVP estimate data
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#'
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#' Example data of fully-vaccinated persons (FVPs).
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#'
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#' @format ## `eg_fvps`
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#' A data frame with 11 rows and 24 columns:
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#' \describe{
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#' \item{scenario_type}{Scenario type name.}
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#' \item{scenario_type_description}{Scenario type description string.}
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#' \item{scenario}{Scenario name string.}
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#' \item{scenario_description}{Scenario description string.}
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#' \item{coverage_set}{Coverage set string.}
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#' \item{gavi_support_level}{String for whether GAVI supported the scenario.}
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#' \item{source_from}{String identifier for the source.}
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#' \item{disease}{Infection identifier.}
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#' \item{vaccine}{Vaccine identifier.}
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#' \item{activity_type}{Vaccination activity identifier.}
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#' \item{year}{Year}
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#' \item{country}{Country name in short format; this is a placeholder name.}
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#' \item{age_from}{Age limit lower limit.}
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#' \item{age_to}{Age limit upper limit.}
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#' \item{age_range_verbatim}{Description of age range.}
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#' \item{target}{Target for vaccination.}
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#' \item{coverage}{Proportional coverage.}
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#' \item{gender}{Sex to which data applies, may be "Male", "Female", or
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#' "Both".}
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#' \item{proportion_risk}{Proportional risk value.}
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#' \item{job}{Job code as a numeric.}
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#' \item{fvps}{Count of FVPs.}
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#' \item{fvps_adjusted}{Count of adjusted FVPs.}
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#' \item{target_adjusted}{Adjusted vaccination target.}
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#' \item{coverage_adjusted}{Ratio of adjusted FVPs to adjusted target.}
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#' }
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#'
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#' @keywords data
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#'
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#' @source Prepared by the VIMC secretariat.
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"eg_fvps"

‎R/plotting_prep.R‎

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@@ -215,7 +215,8 @@ prep_plot_coverage_set <- function(coverage) {
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"delivery",
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"country",
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"year",
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"coverage"
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"coverage",
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"disease"
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)
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coverage_set <- dplyr::select(
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coverage_set,
@@ -226,6 +227,21 @@ prep_plot_coverage_set <- function(coverage) {
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no_vax
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)
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# NOTE: edit factor levels for scenario description
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# Set "No vaccination" as first factor level
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coverage_set <- dplyr::mutate(
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coverage_set,
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scenario_description = stringr::str_replace(
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.data$scenario_description,
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"-|_",
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" "
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),
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scenario_description = forcats::fct_relevel(
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.data$scenario_description,
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"No vaccination"
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)
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)
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coverage_set
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}
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@@ -266,8 +282,12 @@ prep_plot_fvp <- function(fvp, year_min, year_max) {
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) # convert characters to factors and set first level
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)
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fvp_final$scenario <- gsub(tolower(fvp$disease[1L]), "", fvp_final$scenario)
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fvp_final$scenario <- gsub("-", " ", fvp_final$scenario, fixed = TRUE)
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# NOTE: renaming fvp_final$scenario to replace - and _ with spaces
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fvp_final$scenario <- stringr::str_replace(
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fvp_final$scenario,
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"_|-",
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" "
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)
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# determine scenario order in terms of total adjusted FVPs per scenario
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scenario_order <- names(sort(
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fvp_final$scenario <- forcats::fct_relevel(fvp_final$scenario, scenario_order)
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fvp_agg <- dplyr::summarise(
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fvp_final,
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fvp = sum(.data$fvps_adjusted, na.rm = TRUE),
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.groups = c("year", "scenario", "disease")
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.by = c("year", "scenario", "disease")
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)
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fvp_agg

‎data-raw/eg_coverage.R‎

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## code to prepare `eg_coverage` dataset goes here
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eg_coverage <- readr::read_csv(
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"inst/extdata/coverage_scenarios.csv",
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show_col_types = FALSE
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)
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usethis::use_data(eg_coverage, overwrite = TRUE)

‎data-raw/eg_fvps.R‎

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## code to prepare `eg_fvps` dataset goes here
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eg_fvps <- readr::read_csv(
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"inst/extdata/coverage_scenarios.csv",
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show_col_types = FALSE
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)
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# add cols to make eg_FVPs data
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eg_fvps$target <- seq(1.5e6, 1.6e6, length.out = nrow(eg_fvps))
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eg_fvps$job <- 1
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eg_fvps$fvps <- seq(9e5, 1.5e6, length.out = nrow(eg_fvps))
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eg_fvps$fvps_adjusted <- eg_fvps$fvps # assumed same as FVPs
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eg_fvps$target_adjusted <- eg_fvps$target # assumed same as target
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eg_fvps$coverage_adjusted <- eg_fvps$fvps_adjusted / eg_fvps$target_adjusted
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usethis::use_data(eg_fvps, overwrite = TRUE)

‎data/eg_coverage.rda‎

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‎data/eg_fvps.rda‎

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‎inst/WORDLIST‎

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@@ -2,7 +2,9 @@ BMC
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Codecov
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DALYs
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Echeverria
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FVP
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FVPs
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GAVI
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GHA
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Hallett
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Jaspreet
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WPP
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YLLs
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autogenerated
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facetted
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ggplot
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pkgdown
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tibble
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tibbles
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timeseries

‎inst/extdata/coverage_scenarios.csv‎

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"scenario_type","scenario_type_description","scenario","scenario_description","coverage_set","gavi_support_level","source_from","disease","vaccine","activity_type","year","country","age_from","age_to","age_range_verbatim","target","coverage","gender","proportion_risk"
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2030,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2031,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2032,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2033,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2034,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2035,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2036,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2037,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2038,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2039,"RFP",0,0,"default routine age","NA",0.8,"Both",1
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"disease_rout","Routine Only","disease_rout","disease_rout","disease : disease , routine ,with: disease_rout","with","scenario_source","disease","disease","routine",2040,"RFP",0,0,"default routine age","NA",0.8,"Both",1

‎man/eg_coverage.Rd‎

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‎man/eg_fvps.Rd‎

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