R/internal_functions.R
In EU-ECDC/EpiSignalDetection: Signal Detection Analysis
Defines functions
importAtlasExport
cleanAtlasExport
studyPeriod
filterAtlasExport
aggAtlasExport
stsSD
algoSD
plotSD
Documented in
aggAtlasExport
algoSD
cleanAtlasExport
filterAtlasExport
importAtlasExport
plotSD
stsSD
studyPeriod
#' Import ECDC Atlas export file (csv)
#'
#' Import ECDC Atlas csv export file \cr
#' (exported from the online ECDC Atlas:
#' \href{http://atlas.ecdc.europa.eu/public/index.aspx}{http://atlas.ecdc.europa.eu/public/index.aspx}) \cr
#' e.g. "ECDC_surveillance_data_Anthrax.csv" \cr
#'
#'
#' The function will interpret missing reports '-' as NA values
#'
#'
#' @param x file name of a csv file, export from the ECDC Atlas
#'
#' (e.g. \code{x = 'ECDC_surveillance_data_Anthrax.csv'})
#'
#'
#' @return dataframe
#'
#' @usage importAtlasExport(x)
#'
#' @examples
#' \donttest{
#' dataset <- importAtlasExport(x = 'ECDC_surveillance_data_Anthrax.csv')
#' }
#'
#'
#' @seealso \code{\link{cleanAtlasExport}}
#'
#' @export
importAtlasExport <- function(x) {
utils::read.table(x,
header = TRUE,
sep = ",",
quote = "\"",
stringsAsFactors = FALSE,
na.strings = "-") ##Please note that a missing report '-' will be NA
}
#' Clean the Atlas export dataframe
#'
#' Clean the Atlas data export dataframe before signal detection analysis \cr
#' (see \code{\link{importAtlasExport}} and online ECDC Atlas:
#' \href{http://atlas.ecdc.europa.eu/public/index.aspx}{http://atlas.ecdc.europa.eu/public/index.aspx}) \cr
#'
#'
#' The function will: \cr
#' \itemize{
#' \item Filter only on case based indicators i.e. 'Reported Cases"
#' \item Create four additional time variables to ease the analysis: \cr
#' TimeUnit ('Year', 'Month', 'Week'), \cr
#' TimeYear (xxxx), \cr
#' TimeMonth (xx) \cr
#' TimeWeek(xx)
#' \item Keep only variables of interest i.e. "HealthTopic", "Population", "Time", "RegionName", "NumValue"
#' }
#'
#'
#'
#' @param x dataframe, usually the ouput of the import function \code{importAtlasExport(x)}
#'
#' @return dataframe
#'
#' @usage cleanAtlasExport(x)
#'
#' @seealso \code{\link{importAtlasExport}} \code{\link{filterAtlasExport}}
#'
#' @examples
#' \donttest{
#' dataset <- cleanAtlasExport( importAtlasExport(x = 'ECDC_surveillance_data_Anthrax.csv') )
#' }
#'
#'
#' @export
cleanAtlasExport <- function(x) {
#-- Keep indicator 'Reported cases' only
# x <- dplyr::filter(x, x$Indicator == "Reported cases")
x <- dplyr::filter(x, x$Unit == "N")
#-- Keep variables of interest
x <- dplyr::select(x, c("HealthTopic", "Population",
"Indicator", "Time",
"RegionName", "NumValue"))
#-- Formating Time Unit variable for Date variables
x$TimeUnit <- ifelse(grepl("[0-9]{4}", x$Time),
"Year", NA)
x$TimeUnit <- ifelse(grepl("[0-9]{4}-[0-9]{2}", x$Time),
"Month", x$TimeUnit)
x$TimeUnit <- ifelse(grepl("[0-9]{4}-W[0-9]{2}", x$Time),
"Week", x$TimeUnit)
#-- Formating Date variables
x$TimeYear <- ifelse(x$TimeUnit == "Year",
as.numeric(substr(x$Time, 1, 4)), NA)
x$TimeYear <- ifelse(x$TimeUnit == "Month",
as.numeric(substr(x$Time, 1, 4)), x$TimeYear)
x$TimeYear <- ifelse(x$TimeUnit == "Week",
as.numeric(substr(x$Time, 1, 4)), x$TimeYear)
x$TimeMonth <- ifelse(x$TimeUnit == "Month",
as.numeric(substr(x$Time, 6, 8)), NA)
x$TimeWeek <- ifelse(x$TimeUnit == "Week",
substr(x$Time, 7, 9), NA)
#-- Approximating the date
x$TimeDate <- ifelse(x$TimeUnit == "Week",
paste(x$TimeYear, "-W", x$TimeWeek, "-1", sep = ""), NA)
x$TimeDate <- ISOweek::ISOweek2date(x$TimeDate)
#-- Adjusting the month when weekly data
x$TimeMonth <- ifelse(x$TimeUnit == "Week",
as.numeric(format(x$TimeDate, "%m")), x$TimeMonth)
x$TimeDate <- ifelse(x$TimeUnit == "Year",
as.Date(paste(x$TimeYear, "01", "01", sep = "/"), "%Y/%m/%d"),
x$TimeDate)
x$TimeDate <- ifelse(x$TimeUnit == "Month",
as.Date(paste(x$TimeYear, x$TimeMonth, "01", sep = "/"), "%Y/%m/%d"),
x$TimeDate)
x$TimeDate <- as.Date(x$TimeDate, origin = "1970-01-01")
#-- Converting the week in numeric (string was necessary to convert into approximate the date using ISOweek)
x$TimeWeek <- ifelse(x$TimeUnit == "Week", as.numeric(x$TimeWeek), NA)
x <- dplyr::arrange(x, x$RegionName, x$Population, x$TimeYear, x$HealthTopic)
return(x)
}
#' Compute the study period
#'
#' Compute a dataframe including two types of dates corresponding
#' to the study period defined in the list of parameters \code{input} \cr
#' (i.e. \code{StudyPeriod} = approximated daily date; \code{Time} = exact date in the format according to the time unit parameter)
#'
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @return Dataframe including the complete time series with no gaps:
#' \item{StudyPeriod}{approximated daily date e.g. \code{2010-01-01}}
#' \item{Time}{exact date in the format according to the time unit parameter e.g. \code{2010-01}}
#'
#' @usage studyPeriod(input)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' StudyPeriod <- studyPeriod(input)
#' head(StudyPeriod)
#'
#' @export
studyPeriod <- function(input){
#--- Defining the study period
if (input$unit %in% c("Year", "Month", "Week")) {
Start <- as.Date(as.character(input$daterange[1]), "%Y-%m-%d")
if (input$unit == "Month") {
Start <- as.Date(paste(substr(as.character(input$daterange[1]), 1, 7), "-01", sep="" ),
"%Y-%m-%d")
}
End <- as.Date(as.character(input$daterange[2]), "%Y-%m-%d")
StudyPeriod <- seq(Start, End, tolower(input$unit) )
#--- Filtering on the study period
if (input$unit == "Week") {
StudyPeriod <- dplyr::mutate(as.data.frame(StudyPeriod),
Time = ISOweek::ISOweek(StudyPeriod))
}
if (input$unit == "Month") {
StudyPeriod <- dplyr::mutate(as.data.frame(StudyPeriod),
Time = format(StudyPeriod, "%Y-%m"))
}
if (input$unit == "Year") {
StudyPeriod <- dplyr::mutate(as.data.frame(StudyPeriod),
Time = format(StudyPeriod, "%Y"))
}
return(StudyPeriod)
} else { return() }
}
#' Filter clean Atlas export
#'
#' Filter clean Atlas export according to input parameters
#'
#' @param x dataframe, clean Atlas export (see \code{\link{cleanAtlasExport}})
#'
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @param stratified a logical value indicating whether the report
#' should be stratified by \code{Population} variable or not (default \code{FALSE})
#'
#' @usage filterAtlasExport(x, input, stratified)
#'
#' @return dataframe filtered on the selected parameters (input list)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input, stratified = FALSE)
#'
#' @seealso \code{\link{cleanAtlasExport}} \code{\link{aggAtlasExport}}
#'
#' @export
filterAtlasExport <- function(x, input, stratified){
#--- Default value
if (missing(stratified)) {
stratified <- FALSE
}
#--- Filtering on disease
x <- dplyr::filter(x, x$HealthTopic == input$disease)
#--- Filtering on country
if (input$country != "EU-EEA - complete series") {
x <- dplyr::filter(x, x$RegionName == input$country)
}
#--- Filtering on indicator
x <- dplyr::filter(x, x$Indicator == input$indicator)
#--- Filtering on stratification level for not stratified reports
if (stratified == FALSE) {
x <- dplyr::filter(x, x$Population == input$stratification)
}
#--- Filtering on time unit
x <- dplyr::filter(x, x$TimeUnit == input$unit)
#--- Filtering on Study period
StudyPeriod <- studyPeriod(input)
RegionName <- data.frame(RegionName = unique(x$RegionName))
StudyPeriod <- merge(StudyPeriod, RegionName, by = NULL)
StudyPeriod$RegionName <- as.character(StudyPeriod$RegionName)
if ( sum(x$Time %in% StudyPeriod$Time) != length(x$Time)){
x <- dplyr::filter(x, x$Time %in% StudyPeriod$Time )
}
#--- Adding NA when gaps in Time Series
if ( !("StudyPeriod" %in% names(x)) ) {
x <- dplyr::left_join(StudyPeriod, x, by=c("Time","RegionName"))
}
return(x)
}
#' Aggregate filtered final Atlas export
#'
#' Aggregate filtered final Atlas export
#'
#' @param x dataframe
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @return dataframe aggregated by geographical level and time unit
#'
#' @usage aggAtlasExport(x, input)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input)
#'
#' #-- Aggregating the data by geographical level and time point
#' dataset <- aggAtlasExport(dataset, input)
#'
#' @seealso \code{\link{filterAtlasExport}} \code{\link{SignalData}} \code{\link{stsSD}}
#'
#' @export
aggAtlasExport <- function(x, input){
#-- Excluding countries with gaps and EU pre-computed values from the TS
excluded <- unique(x$RegionName[ is.na(x$NumValue) ])
if (input$country == "EU-EEA - complete series") {
included <- unique(x$RegionName[!(x$RegionName %in% c(excluded, "EU", "EU/EEA"))])
if (length(included) != 0) {
x <- dplyr::filter(x, x$RegionName %in% included )
} else {
warning("All countries present with at least one gap in the time series")
}
} else if (length(excluded) !=0) {
warning("The selected country presents with at least one gap in the time series")
}
#-- Aggregation by time and place
RegionName <- NumValue <- NULL
x <- dplyr::group_by_(x, c("StudyPeriod") )
x <- dplyr::summarise(x,
"CasesByCountry" = paste(RegionName, " (", NumValue, ")", sep = "", collapse = ", "),
"NumValue" = sum(NumValue, na.rm = TRUE) )
x <- dplyr::ungroup(x)
x <- dplyr::arrange(x, x$StudyPeriod)
return(x)
}
#' Build sts object
#'
#' Build sts surveillance object
#'
#' @param observedCases numeric vector of the number of cases by time unit (y axis of the time series)
#' @param studyPeriod vector of dates of length(obeservedCases) (x axis of the time series)
#' @param timeUnit character string for the time unit of the time series. Options are Week or Month.
#' @param startYM numeric vector including Year and Month of start of the historical data
#' @return sts
#' @usage stsSD(observedCases, studyPeriod, timeUnit = "Month", startYM = c(2000, 1) )
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input)
#'
#' #-- Aggregating the data by geographical level and time point
#' dataset <- aggAtlasExport(dataset, input)
#'
#' #-- Bulding the corresponding sts object
#' dataset.sts <- stsSD(observedCases = dataset$NumValue,
#' studyPeriod = dataset$StudyPeriod,
#' timeUnit = input$unit,
#' startYM = c(as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%Y")),
#' as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%m"))))
#'
#' @seealso \code{\link{aggAtlasExport}} \code{\link{algoSD}}
#'
#' @export
stsSD <- function(observedCases, studyPeriod, timeUnit = "Month", startYM = c(2000, 1) ){
if (timeUnit == "Month") {
fq <- 12
} else if (timeUnit == "Week") { #This is a proxy for the number of ISO weeks in a year
fq <- 52
} else {
fq <- 1
}
x.sts <- surveillance::sts( observed = observedCases,
start = startYM,
freq = fq,
epochAsDate = TRUE,
epoch = as.numeric(studyPeriod))
return(x.sts)
}
#' Build algo object
#'
#' Build algo object from an sts object class
#' using either FarringtonFlexible or GLRNB surveillance algorithm
#'
#' @param x.sts sts class object (see \code{\link{stsSD}} output)
#' @param algo character string containing the name of the algorithm to use.
#' Options are "FarringtonFlexible" (default) or "GLRNB".
#' @param timeUnit character string for the time unit of the time series. Options are "Week" or "Month".
#' @param testingPeriod numeric: number of time units (months, weeks) back in time to test the algorithm on
#' (to detect outbreaks in)
#'
#' @return sts
#'
#' @usage algoSD(x.sts, algo = "FarringtonFlexible", timeUnit = "Month", testingPeriod = 5)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input)
#'
#' #-- Aggregating the data by geographical level and time point
#' dataset <- aggAtlasExport(dataset, input)
#'
#' #-- Bulding the corresponding sts object
#' dataset.sts <- stsSD(observedCases = dataset$NumValue,
#' studyPeriod = dataset$StudyPeriod,
#' timeUnit = input$unit,
#' startYM = c(as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%Y")),
#' as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%m"))))
#'
#' #-- Building the corresponding algo object
#' dataset.algo <- algoSD(dataset.sts,
#' algo = input$algo,
#' timeUnit = input$unit,
#' testingPeriod =
#' input$testingperiod)
#'
#' @seealso \code{\link{stsSD}} \code{\link{plotSD}}
#'
#' @export
algoSD <- function(x.sts, algo = "FarringtonFlexible",
timeUnit = "Month", testingPeriod = 5){
#-- Retrieving algorithm parameters to use according to the selected time unit
params <- EpiSignalDetection::AlgoParam[[algo]]
params <- dplyr::filter(params, params$timeunit == tolower(timeUnit))
TestingPeriodTS <- ((length(x.sts@epoch) - testingPeriod + 1):length(x.sts@epoch))
#-------------------------------
#--- FarringtonFlexible
#-------------------------------
if (algo == "FarringtonFlexible") {
#-- Estimating b parameter
if (timeUnit == "Month") {
b <- floor( ( utils::tail(x.sts@epoch,1) - x.sts@epoch[1] ) / 365.25 -
testingPeriod / 12)
}
if (timeUnit == "Week") {
b <- floor( ( utils::tail(x.sts@epoch,1) - x.sts@epoch[1] ) / 365.25 -
testingPeriod / 52)
}
#-- Computing control parameter
control <- list( range = TestingPeriodTS,
b = b,
w = params$w,
reweight = params$reweight,
trend = params$trend,
plot = FALSE,
weightsThreshold = params$weightsThreshold,
glmWarnings = params$glmWarnings,
pThresholdTrend = params$pThresholdTrend,
limit54 = c(params$limit54_1, params$limit54_2))
#-- Error message if the algorithm fails
shiny::validate(
shiny::need(try(surveillance::farringtonFlexible(x.sts, control = control)),
"\n \n \n
--- The Farrington algorithm cannot proceed. --- \n
\n \n \n")
)
x.algo <- surveillance::farringtonFlexible( x.sts , control = control)
return(x.algo)
}
#-------------------------------
#--- GLRNB
#-------------------------------
if(algo == "GLRNB"){
control <- list( range = TestingPeriodTS,
c.ARL = params$cARL,
mu0 = eval( parse( text = params$mu0)) ,
alpha = eval( parse( text = params$alpha)),
Mtilde = params$Mtilde,
M = params$M,
change = params$Change,
theta = eval( parse( text = params$theta )) ,
ret = "cases")
#--- Error message in case the algorithm fails
shiny::validate(
shiny::need(try(surveillance::glrnb(x.sts, control = control)),
"\n \n \n
--- The GLRNB algorithm cannot proceed. --- \n
\n \n \n")
)
x.algo <- surveillance::glrnb(x.sts, control = control)
}
return(x.algo)
}
#' Plot the Signal Detection time series
#'
#' Plot the Signal Detection time series including historical data, alarm detection period and alarms
#'
#' @param x dataframe (default \code{\link{SignalData}})
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @param subRegionName character string, region label to use in the plot, if different than \code{input$RegionName} (optional)
#' @param x.sts sts object (optional), see \code{\link{stsSD}})
#' @param x.algo algo object (optional), see \code{\link{algoSD}})
#'
#' @return plot
#'
#' @usage plotSD(x, input, subRegionName, x.sts, x.algo)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Plotting the signal detection output
#' plotSD(input = input)
#'
#' @seealso \code{\link{SignalData}} \code{\link{runEpiSDApp}}
#' @export
plotSD <- function(x, input, subRegionName, x.sts, x.algo){
# ---
# Setting default arguments if missing
# ---
if (missing(x)) {
x <- EpiSignalDetection::SignalData
x <- filterAtlasExport(x, input)
x <- aggAtlasExport(x, input)
}
if (missing(subRegionName)) {
subRegionName <- input$country
}
if (missing(x.sts)) {
x.sts <- stsSD(observedCases = x$NumValue,
studyPeriod = x$StudyPeriod,
timeUnit = input$unit,
startYM = c(as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%Y")),
as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%m"))))
}
params <- EpiSignalDetection::AlgoParam[[input$algo]]
params <- dplyr::filter(params, params$timeunit == tolower(input$unit))
if (missing(x.algo)){
x.algo <- algoSD(x.sts,
algo = input$algo,
timeUnit = input$unit,
testingPeriod = input$testingperiod)
}
#----
# Main colors used
#----
COLOR1 <- "#AFD082"
COLOR2 <- "green4"
COLOR3 <- "#CB5F13"
Historical <- (length(x.sts@epoch) - input$testingperiod)
Max <- max(x.sts@observed, x.algo@upperbound, na.rm = TRUE)
Xunit <- ifelse(input$unit == "Month", 1, 4)
graphics::par(xpd = TRUE, cex.main = 1.6, cex.axis = 1,
las = 1, mar = c(7.1,4.1,4.1,2.1))
graphics::plot( x.sts@observed[1:Historical],
ty = "l",
lwd = 2,
col = COLOR1,
xlim = c(1, length(x.sts@epoch)),
ylim = c(0, Max),
xaxt = "n", bty = "l", yaxs = "i", xaxs = "i",
main = paste( input$disease, " (", tolower(input$indicator), ") ",
tolower(input$stratification), " by ", tolower(input$unit),
",\n in ", subRegionName,
" from ", input$daterange[1]," until ", input$daterange[2], sep=""),
xlab = "",
ylab = "Number of cases")
graphics::title(xlab = paste("Date (by ", tolower(input$unit), ")", sep = ""), line = 5.5)
Xaxis = seq(1, length(x.sts@epoch), by = Xunit)
graphics::axis(1 , Xaxis, x$StudyPeriod, labels = FALSE)
graphics::text(Xaxis,
labels = x$StudyPeriod[Xaxis],
graphics::par("usr")[3],
srt = 90,
xpd = TRUE,
cex = 0.9 ,
pos = 1,
offset = 2.9)
graphics::lines(y = x.algo@observed ,
x = ((Historical + 1):(length(x.sts@epoch) )),
ty = "l" ,
lty = 5,
lwd = 2,
col = COLOR1)
graphics::lines(y = x.sts@observed[(Historical):(Historical+1)] ,
x = ((Historical):(Historical+1)),
ty = "l" ,
lty = 2,
lwd = 1,
col = COLOR1)
graphics::lines(y = c(x.sts@observed[Historical],x.algo@upperbound[1] ) ,
x = ((Historical):(Historical+1)),
ty = "l" ,
lty = 2,
lwd = 1,
col = COLOR3)
graphics::lines(y = x.algo@upperbound,
x = ((Historical + 1):(length(x.sts@epoch) )),
ty= "l" ,
lwd = 2,
col = COLOR3 )
graphics::points(x = Historical + which(x.algo@alarm == TRUE),
y = rep(-0.05, sum(x.algo@alarm, na.rm = TRUE)),
type = "p",
pch = 17,
col = 2)
graphics::legend(x = "topright",
cex = 1,
bty = 'n',
inset = c(0.01,-0.2),
c("Historical data", "Observed cases", "Threshold", "Alarms"),
lty = c(1,5,1,NA),
pch = c(NA,NA,NA,17),
lwd = 2,
col = c(COLOR1,COLOR1,COLOR3,"red"))
}
EU-ECDC/EpiSignalDetection documentation built on May 26, 2019, 2:36 a.m.
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Defines functions importAtlasExport cleanAtlasExport studyPeriod filterAtlasExport aggAtlasExport stsSD algoSD plotSD
Documented in aggAtlasExport algoSD cleanAtlasExport filterAtlasExport importAtlasExport plotSD stsSD studyPeriod
#' Import ECDC Atlas export file (csv)
#'
#' Import ECDC Atlas csv export file \cr
#' (exported from the online ECDC Atlas:
#' \href{http://atlas.ecdc.europa.eu/public/index.aspx}{http://atlas.ecdc.europa.eu/public/index.aspx}) \cr
#' e.g. "ECDC_surveillance_data_Anthrax.csv" \cr
#'
#'
#' The function will interpret missing reports '-' as NA values
#'
#'
#' @param x file name of a csv file, export from the ECDC Atlas
#'
#' (e.g. \code{x = 'ECDC_surveillance_data_Anthrax.csv'})
#'
#'
#' @return dataframe
#'
#' @usage importAtlasExport(x)
#'
#' @examples
#' \donttest{
#' dataset <- importAtlasExport(x = 'ECDC_surveillance_data_Anthrax.csv')
#' }
#'
#'
#' @seealso \code{\link{cleanAtlasExport}}
#'
#' @export
importAtlasExport <- function(x) {
utils::read.table(x,
header = TRUE,
sep = ",",
quote = "\"",
stringsAsFactors = FALSE,
na.strings = "-") ##Please note that a missing report '-' will be NA
}
#' Clean the Atlas export dataframe
#'
#' Clean the Atlas data export dataframe before signal detection analysis \cr
#' (see \code{\link{importAtlasExport}} and online ECDC Atlas:
#' \href{http://atlas.ecdc.europa.eu/public/index.aspx}{http://atlas.ecdc.europa.eu/public/index.aspx}) \cr
#'
#'
#' The function will: \cr
#' \itemize{
#' \item Filter only on case based indicators i.e. 'Reported Cases"
#' \item Create four additional time variables to ease the analysis: \cr
#' TimeUnit ('Year', 'Month', 'Week'), \cr
#' TimeYear (xxxx), \cr
#' TimeMonth (xx) \cr
#' TimeWeek(xx)
#' \item Keep only variables of interest i.e. "HealthTopic", "Population", "Time", "RegionName", "NumValue"
#' }
#'
#'
#'
#' @param x dataframe, usually the ouput of the import function \code{importAtlasExport(x)}
#'
#' @return dataframe
#'
#' @usage cleanAtlasExport(x)
#'
#' @seealso \code{\link{importAtlasExport}} \code{\link{filterAtlasExport}}
#'
#' @examples
#' \donttest{
#' dataset <- cleanAtlasExport( importAtlasExport(x = 'ECDC_surveillance_data_Anthrax.csv') )
#' }
#'
#'
#' @export
cleanAtlasExport <- function(x) {
#-- Keep indicator 'Reported cases' only
# x <- dplyr::filter(x, x$Indicator == "Reported cases")
x <- dplyr::filter(x, x$Unit == "N")
#-- Keep variables of interest
x <- dplyr::select(x, c("HealthTopic", "Population",
"Indicator", "Time",
"RegionName", "NumValue"))
#-- Formating Time Unit variable for Date variables
x$TimeUnit <- ifelse(grepl("[0-9]{4}", x$Time),
"Year", NA)
x$TimeUnit <- ifelse(grepl("[0-9]{4}-[0-9]{2}", x$Time),
"Month", x$TimeUnit)
x$TimeUnit <- ifelse(grepl("[0-9]{4}-W[0-9]{2}", x$Time),
"Week", x$TimeUnit)
#-- Formating Date variables
x$TimeYear <- ifelse(x$TimeUnit == "Year",
as.numeric(substr(x$Time, 1, 4)), NA)
x$TimeYear <- ifelse(x$TimeUnit == "Month",
as.numeric(substr(x$Time, 1, 4)), x$TimeYear)
x$TimeYear <- ifelse(x$TimeUnit == "Week",
as.numeric(substr(x$Time, 1, 4)), x$TimeYear)
x$TimeMonth <- ifelse(x$TimeUnit == "Month",
as.numeric(substr(x$Time, 6, 8)), NA)
x$TimeWeek <- ifelse(x$TimeUnit == "Week",
substr(x$Time, 7, 9), NA)
#-- Approximating the date
x$TimeDate <- ifelse(x$TimeUnit == "Week",
paste(x$TimeYear, "-W", x$TimeWeek, "-1", sep = ""), NA)
x$TimeDate <- ISOweek::ISOweek2date(x$TimeDate)
#-- Adjusting the month when weekly data
x$TimeMonth <- ifelse(x$TimeUnit == "Week",
as.numeric(format(x$TimeDate, "%m")), x$TimeMonth)
x$TimeDate <- ifelse(x$TimeUnit == "Year",
as.Date(paste(x$TimeYear, "01", "01", sep = "/"), "%Y/%m/%d"),
x$TimeDate)
x$TimeDate <- ifelse(x$TimeUnit == "Month",
as.Date(paste(x$TimeYear, x$TimeMonth, "01", sep = "/"), "%Y/%m/%d"),
x$TimeDate)
x$TimeDate <- as.Date(x$TimeDate, origin = "1970-01-01")
#-- Converting the week in numeric (string was necessary to convert into approximate the date using ISOweek)
x$TimeWeek <- ifelse(x$TimeUnit == "Week", as.numeric(x$TimeWeek), NA)
x <- dplyr::arrange(x, x$RegionName, x$Population, x$TimeYear, x$HealthTopic)
return(x)
}
#' Compute the study period
#'
#' Compute a dataframe including two types of dates corresponding
#' to the study period defined in the list of parameters \code{input} \cr
#' (i.e. \code{StudyPeriod} = approximated daily date; \code{Time} = exact date in the format according to the time unit parameter)
#'
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @return Dataframe including the complete time series with no gaps:
#' \item{StudyPeriod}{approximated daily date e.g. \code{2010-01-01}}
#' \item{Time}{exact date in the format according to the time unit parameter e.g. \code{2010-01}}
#'
#' @usage studyPeriod(input)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' StudyPeriod <- studyPeriod(input)
#' head(StudyPeriod)
#'
#' @export
studyPeriod <- function(input){
#--- Defining the study period
if (input$unit %in% c("Year", "Month", "Week")) {
Start <- as.Date(as.character(input$daterange[1]), "%Y-%m-%d")
if (input$unit == "Month") {
Start <- as.Date(paste(substr(as.character(input$daterange[1]), 1, 7), "-01", sep="" ),
"%Y-%m-%d")
}
End <- as.Date(as.character(input$daterange[2]), "%Y-%m-%d")
StudyPeriod <- seq(Start, End, tolower(input$unit) )
#--- Filtering on the study period
if (input$unit == "Week") {
StudyPeriod <- dplyr::mutate(as.data.frame(StudyPeriod),
Time = ISOweek::ISOweek(StudyPeriod))
}
if (input$unit == "Month") {
StudyPeriod <- dplyr::mutate(as.data.frame(StudyPeriod),
Time = format(StudyPeriod, "%Y-%m"))
}
if (input$unit == "Year") {
StudyPeriod <- dplyr::mutate(as.data.frame(StudyPeriod),
Time = format(StudyPeriod, "%Y"))
}
return(StudyPeriod)
} else { return() }
}
#' Filter clean Atlas export
#'
#' Filter clean Atlas export according to input parameters
#'
#' @param x dataframe, clean Atlas export (see \code{\link{cleanAtlasExport}})
#'
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @param stratified a logical value indicating whether the report
#' should be stratified by \code{Population} variable or not (default \code{FALSE})
#'
#' @usage filterAtlasExport(x, input, stratified)
#'
#' @return dataframe filtered on the selected parameters (input list)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input, stratified = FALSE)
#'
#' @seealso \code{\link{cleanAtlasExport}} \code{\link{aggAtlasExport}}
#'
#' @export
filterAtlasExport <- function(x, input, stratified){
#--- Default value
if (missing(stratified)) {
stratified <- FALSE
}
#--- Filtering on disease
x <- dplyr::filter(x, x$HealthTopic == input$disease)
#--- Filtering on country
if (input$country != "EU-EEA - complete series") {
x <- dplyr::filter(x, x$RegionName == input$country)
}
#--- Filtering on indicator
x <- dplyr::filter(x, x$Indicator == input$indicator)
#--- Filtering on stratification level for not stratified reports
if (stratified == FALSE) {
x <- dplyr::filter(x, x$Population == input$stratification)
}
#--- Filtering on time unit
x <- dplyr::filter(x, x$TimeUnit == input$unit)
#--- Filtering on Study period
StudyPeriod <- studyPeriod(input)
RegionName <- data.frame(RegionName = unique(x$RegionName))
StudyPeriod <- merge(StudyPeriod, RegionName, by = NULL)
StudyPeriod$RegionName <- as.character(StudyPeriod$RegionName)
if ( sum(x$Time %in% StudyPeriod$Time) != length(x$Time)){
x <- dplyr::filter(x, x$Time %in% StudyPeriod$Time )
}
#--- Adding NA when gaps in Time Series
if ( !("StudyPeriod" %in% names(x)) ) {
x <- dplyr::left_join(StudyPeriod, x, by=c("Time","RegionName"))
}
return(x)
}
#' Aggregate filtered final Atlas export
#'
#' Aggregate filtered final Atlas export
#'
#' @param x dataframe
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @return dataframe aggregated by geographical level and time unit
#'
#' @usage aggAtlasExport(x, input)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input)
#'
#' #-- Aggregating the data by geographical level and time point
#' dataset <- aggAtlasExport(dataset, input)
#'
#' @seealso \code{\link{filterAtlasExport}} \code{\link{SignalData}} \code{\link{stsSD}}
#'
#' @export
aggAtlasExport <- function(x, input){
#-- Excluding countries with gaps and EU pre-computed values from the TS
excluded <- unique(x$RegionName[ is.na(x$NumValue) ])
if (input$country == "EU-EEA - complete series") {
included <- unique(x$RegionName[!(x$RegionName %in% c(excluded, "EU", "EU/EEA"))])
if (length(included) != 0) {
x <- dplyr::filter(x, x$RegionName %in% included )
} else {
warning("All countries present with at least one gap in the time series")
}
} else if (length(excluded) !=0) {
warning("The selected country presents with at least one gap in the time series")
}
#-- Aggregation by time and place
RegionName <- NumValue <- NULL
x <- dplyr::group_by_(x, c("StudyPeriod") )
x <- dplyr::summarise(x,
"CasesByCountry" = paste(RegionName, " (", NumValue, ")", sep = "", collapse = ", "),
"NumValue" = sum(NumValue, na.rm = TRUE) )
x <- dplyr::ungroup(x)
x <- dplyr::arrange(x, x$StudyPeriod)
return(x)
}
#' Build sts object
#'
#' Build sts surveillance object
#'
#' @param observedCases numeric vector of the number of cases by time unit (y axis of the time series)
#' @param studyPeriod vector of dates of length(obeservedCases) (x axis of the time series)
#' @param timeUnit character string for the time unit of the time series. Options are Week or Month.
#' @param startYM numeric vector including Year and Month of start of the historical data
#' @return sts
#' @usage stsSD(observedCases, studyPeriod, timeUnit = "Month", startYM = c(2000, 1) )
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input)
#'
#' #-- Aggregating the data by geographical level and time point
#' dataset <- aggAtlasExport(dataset, input)
#'
#' #-- Bulding the corresponding sts object
#' dataset.sts <- stsSD(observedCases = dataset$NumValue,
#' studyPeriod = dataset$StudyPeriod,
#' timeUnit = input$unit,
#' startYM = c(as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%Y")),
#' as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%m"))))
#'
#' @seealso \code{\link{aggAtlasExport}} \code{\link{algoSD}}
#'
#' @export
stsSD <- function(observedCases, studyPeriod, timeUnit = "Month", startYM = c(2000, 1) ){
if (timeUnit == "Month") {
fq <- 12
} else if (timeUnit == "Week") { #This is a proxy for the number of ISO weeks in a year
fq <- 52
} else {
fq <- 1
}
x.sts <- surveillance::sts( observed = observedCases,
start = startYM,
freq = fq,
epochAsDate = TRUE,
epoch = as.numeric(studyPeriod))
return(x.sts)
}
#' Build algo object
#'
#' Build algo object from an sts object class
#' using either FarringtonFlexible or GLRNB surveillance algorithm
#'
#' @param x.sts sts class object (see \code{\link{stsSD}} output)
#' @param algo character string containing the name of the algorithm to use.
#' Options are "FarringtonFlexible" (default) or "GLRNB".
#' @param timeUnit character string for the time unit of the time series. Options are "Week" or "Month".
#' @param testingPeriod numeric: number of time units (months, weeks) back in time to test the algorithm on
#' (to detect outbreaks in)
#'
#' @return sts
#'
#' @usage algoSD(x.sts, algo = "FarringtonFlexible", timeUnit = "Month", testingPeriod = 5)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Example dataset
#' dataset <- EpiSignalDetection::SignalData
#'
#' #-- Filtering on declared input parameters
#' dataset <- filterAtlasExport(dataset, input)
#'
#' #-- Aggregating the data by geographical level and time point
#' dataset <- aggAtlasExport(dataset, input)
#'
#' #-- Bulding the corresponding sts object
#' dataset.sts <- stsSD(observedCases = dataset$NumValue,
#' studyPeriod = dataset$StudyPeriod,
#' timeUnit = input$unit,
#' startYM = c(as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%Y")),
#' as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%m"))))
#'
#' #-- Building the corresponding algo object
#' dataset.algo <- algoSD(dataset.sts,
#' algo = input$algo,
#' timeUnit = input$unit,
#' testingPeriod =
#' input$testingperiod)
#'
#' @seealso \code{\link{stsSD}} \code{\link{plotSD}}
#'
#' @export
algoSD <- function(x.sts, algo = "FarringtonFlexible",
timeUnit = "Month", testingPeriod = 5){
#-- Retrieving algorithm parameters to use according to the selected time unit
params <- EpiSignalDetection::AlgoParam[[algo]]
params <- dplyr::filter(params, params$timeunit == tolower(timeUnit))
TestingPeriodTS <- ((length(x.sts@epoch) - testingPeriod + 1):length(x.sts@epoch))
#-------------------------------
#--- FarringtonFlexible
#-------------------------------
if (algo == "FarringtonFlexible") {
#-- Estimating b parameter
if (timeUnit == "Month") {
b <- floor( ( utils::tail(x.sts@epoch,1) - x.sts@epoch[1] ) / 365.25 -
testingPeriod / 12)
}
if (timeUnit == "Week") {
b <- floor( ( utils::tail(x.sts@epoch,1) - x.sts@epoch[1] ) / 365.25 -
testingPeriod / 52)
}
#-- Computing control parameter
control <- list( range = TestingPeriodTS,
b = b,
w = params$w,
reweight = params$reweight,
trend = params$trend,
plot = FALSE,
weightsThreshold = params$weightsThreshold,
glmWarnings = params$glmWarnings,
pThresholdTrend = params$pThresholdTrend,
limit54 = c(params$limit54_1, params$limit54_2))
#-- Error message if the algorithm fails
shiny::validate(
shiny::need(try(surveillance::farringtonFlexible(x.sts, control = control)),
"\n \n \n
--- The Farrington algorithm cannot proceed. --- \n
\n \n \n")
)
x.algo <- surveillance::farringtonFlexible( x.sts , control = control)
return(x.algo)
}
#-------------------------------
#--- GLRNB
#-------------------------------
if(algo == "GLRNB"){
control <- list( range = TestingPeriodTS,
c.ARL = params$cARL,
mu0 = eval( parse( text = params$mu0)) ,
alpha = eval( parse( text = params$alpha)),
Mtilde = params$Mtilde,
M = params$M,
change = params$Change,
theta = eval( parse( text = params$theta )) ,
ret = "cases")
#--- Error message in case the algorithm fails
shiny::validate(
shiny::need(try(surveillance::glrnb(x.sts, control = control)),
"\n \n \n
--- The GLRNB algorithm cannot proceed. --- \n
\n \n \n")
)
x.algo <- surveillance::glrnb(x.sts, control = control)
}
return(x.algo)
}
#' Plot the Signal Detection time series
#'
#' Plot the Signal Detection time series including historical data, alarm detection period and alarms
#'
#' @param x dataframe (default \code{\link{SignalData}})
#' @param input list of parameters as defined in the Signal Detection Application (see \code{\link{runEpiSDApp}})
#'
#' (i.e. \code{list(disease, country, indicator, stratification, unit, daterange, algo, testingperiod)})
#'
#' @param subRegionName character string, region label to use in the plot, if different than \code{input$RegionName} (optional)
#' @param x.sts sts object (optional), see \code{\link{stsSD}})
#' @param x.algo algo object (optional), see \code{\link{algoSD}})
#'
#' @return plot
#'
#' @usage plotSD(x, input, subRegionName, x.sts, x.algo)
#'
#' @examples
#' #-- Setting the parameters to run the report for
#' input <- list(
#' disease = "Salmonellosis",
#' country = "EU-EEA - complete series",
#' indicator = "Reported cases",
#' stratification = "Confirmed cases",
#' unit = "Month",
#' daterange = c("2010-01-01", "2016-12-31"),
#' algo = "FarringtonFlexible",
#' testingperiod = 5
#' )
#'
#' #-- Plotting the signal detection output
#' plotSD(input = input)
#'
#' @seealso \code{\link{SignalData}} \code{\link{runEpiSDApp}}
#' @export
plotSD <- function(x, input, subRegionName, x.sts, x.algo){
# ---
# Setting default arguments if missing
# ---
if (missing(x)) {
x <- EpiSignalDetection::SignalData
x <- filterAtlasExport(x, input)
x <- aggAtlasExport(x, input)
}
if (missing(subRegionName)) {
subRegionName <- input$country
}
if (missing(x.sts)) {
x.sts <- stsSD(observedCases = x$NumValue,
studyPeriod = x$StudyPeriod,
timeUnit = input$unit,
startYM = c(as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%Y")),
as.numeric(format(as.Date(input$daterange[1], "%Y-%m-%d"), "%m"))))
}
params <- EpiSignalDetection::AlgoParam[[input$algo]]
params <- dplyr::filter(params, params$timeunit == tolower(input$unit))
if (missing(x.algo)){
x.algo <- algoSD(x.sts,
algo = input$algo,
timeUnit = input$unit,
testingPeriod = input$testingperiod)
}
#----
# Main colors used
#----
COLOR1 <- "#AFD082"
COLOR2 <- "green4"
COLOR3 <- "#CB5F13"
Historical <- (length(x.sts@epoch) - input$testingperiod)
Max <- max(x.sts@observed, x.algo@upperbound, na.rm = TRUE)
Xunit <- ifelse(input$unit == "Month", 1, 4)
graphics::par(xpd = TRUE, cex.main = 1.6, cex.axis = 1,
las = 1, mar = c(7.1,4.1,4.1,2.1))
graphics::plot( x.sts@observed[1:Historical],
ty = "l",
lwd = 2,
col = COLOR1,
xlim = c(1, length(x.sts@epoch)),
ylim = c(0, Max),
xaxt = "n", bty = "l", yaxs = "i", xaxs = "i",
main = paste( input$disease, " (", tolower(input$indicator), ") ",
tolower(input$stratification), " by ", tolower(input$unit),
",\n in ", subRegionName,
" from ", input$daterange[1]," until ", input$daterange[2], sep=""),
xlab = "",
ylab = "Number of cases")
graphics::title(xlab = paste("Date (by ", tolower(input$unit), ")", sep = ""), line = 5.5)
Xaxis = seq(1, length(x.sts@epoch), by = Xunit)
graphics::axis(1 , Xaxis, x$StudyPeriod, labels = FALSE)
graphics::text(Xaxis,
labels = x$StudyPeriod[Xaxis],
graphics::par("usr")[3],
srt = 90,
xpd = TRUE,
cex = 0.9 ,
pos = 1,
offset = 2.9)
graphics::lines(y = x.algo@observed ,
x = ((Historical + 1):(length(x.sts@epoch) )),
ty = "l" ,
lty = 5,
lwd = 2,
col = COLOR1)
graphics::lines(y = x.sts@observed[(Historical):(Historical+1)] ,
x = ((Historical):(Historical+1)),
ty = "l" ,
lty = 2,
lwd = 1,
col = COLOR1)
graphics::lines(y = c(x.sts@observed[Historical],x.algo@upperbound[1] ) ,
x = ((Historical):(Historical+1)),
ty = "l" ,
lty = 2,
lwd = 1,
col = COLOR3)
graphics::lines(y = x.algo@upperbound,
x = ((Historical + 1):(length(x.sts@epoch) )),
ty= "l" ,
lwd = 2,
col = COLOR3 )
graphics::points(x = Historical + which(x.algo@alarm == TRUE),
y = rep(-0.05, sum(x.algo@alarm, na.rm = TRUE)),
type = "p",
pch = 17,
col = 2)
graphics::legend(x = "topright",
cex = 1,
bty = 'n',
inset = c(0.01,-0.2),
c("Historical data", "Observed cases", "Threshold", "Alarms"),
lty = c(1,5,1,NA),
pch = c(NA,NA,NA,17),
lwd = 2,
col = c(COLOR1,COLOR1,COLOR3,"red"))
}
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