List of datasets containing the Farrington Flexible and GLRNB default parameters by time unit

Description

A list including two datasets containing the parameters used for Farrington Flexible and for GLRNB for each time unit available in the Signal Detection tool

Usage

AlgoParam

Format

A list of 2 dataframes: one with 2 rows and 9 variables and GRLNB with 2 rows and 8 variables

1. Default parameters for FarringtonFlexible algorithm

   timeunit

   Time units available in the signal detection tool i.e. week, month

   w

   Window's half-size, i.e. number of weeks to include before and after the current week in each year (w=2 for weeks, w=1 for months)

   reweight

   Logical specifying whether to reweight past outbreaks or not (TRUE for both weeks and months, past outbreaks are always reweighted)

   trend

   Logical specifying whether a trend should be included and kept in case the conditions in the Farrington et. al. paper are met. (TRUE for both weeks and months, a trend is always fit)

   weightsThreshold

   Numeric defining the threshold for reweighting past outbreaks using the Anscombe residuals (2.85 for both weeks and months, as advised in the improved method)

   glmWarnings

   Logical specifying whether to print warnings from the call to glm (TRUE for both weeks and months)

   pThresholdTrend

   Numeric defining the threshold for deciding whether to keep trend in the model (0.05 for both weeks and months)

   limit54_1

   Integer, the number of cases defining a threshold for minimum alarm, no alarm is sounded if fewer than 'limit54_1' cases were reported in the past 'limit54_2' weeks/months

   limit54_2

   Integer, the number of periods defining a threshold for minimum alarm, no alarm is sounded if fewer than 'limit54_1' cases were reported in the past 'limit54_2' weeks/months

2. Default parameters for GLRNB algorithm

   timeunit

   Time units available in the signal detection tool i.e. week, month

   mu0

   A vector of in-control values of the mean of the Poisson / negative binomial distribution with the same length as range - NULL for both weeks and months

   theta

   Numeric, the pre-specified value for k or lambda is used in a recursive LR scheme - log(1.2) for both weeks and months corresponding to a 20 percent increase in the mean

   alpha

   Numeric, the dispersion parameter of the negative binomial distribution. If alpha=NULL the parameter is calculated as part of the in-control estimation - alpha=NULL for both weeks and months

   cARL

   Numeric, the threshold in the GLR test, i.e. c_gamma - cARL=0.25 for both weeks and months

   Mtilde

   Integer, the number of observations needed before we have a full rank - Mtilde=1 for both weeks and months

   M

   Integer defining the number of time instances back in time in the window-limited approach. To always look back until the first observation use M=-1. M=1 for both weeks and months

   Change

   Character string specifying the type of the alternative. Currently the two choices are intercept and epi - Change=intercept for both weeks and months

See Also

surveillance::farringtonFlexible surveillance::glrnb

Dataset for Signal Detection Analysis, reported cases, 1999-2018 (ECDC Atlas export)

Description

A dataset containing an export from the ECDC Atlas for salmonellosis and measles data. This export is cleaned and ready for Signal Detection Analysis (see. cleanAtlasExport() )

Usage

SignalData

Format

A data frame with 80,834 rows and 11 variables:

HealthTopic

Disease name e.g. Salmonellosis or Measles

Population

Population characteristics e.g. All cases, Confirmed cases, Serotype AGONA, Serotype BAREILLY etc.

Indicator

Indicator e.g. Hospitalised cases, Reported cases, Number of deaths, etc.

Time

Time variable including both yearly data from 1999 to 2017, and monthly data from 1999-01 to 2018-02

RegionName

Geographical level including country names e.g. Austria, Belgium, Bulgaria, etc.

NumValue

Number of cases

TimeUnit

Time unit corresponding to the format of the date in the 'Time' variable e.g. Year or Month

TimeYear

Year of the date available in the 'Time' variable, regardless of the date format i.e. 1999 to 2018

TimeMonth

Month of the date available in the 'Time' variable, regardless of the date format i.e. 1 to 12

TimeWeek

Week of the date available in the 'Time' variable, regardless of the date format i.e. NA since this dataset does not include any weekly data

TimeDate

Approximated date corresponding to the date available in the 'Time' variable (daily format)

Source

http://atlas.ecdc.europa.eu/public/index.aspx

Aggregate filtered final Atlas export

Description

Aggregate filtered final Atlas export

Usage

aggAtlasExport(x, input)

Arguments

Value

dataframe aggregated by geographical level and time unit

See Also

filterAtlasExport SignalData stsSD

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)

Build algo object

Description

Build algo object from an sts object class using either FarringtonFlexible or GLRNB surveillance algorithm

Usage

algoSD(x.sts, algo = "FarringtonFlexible", timeUnit = "Month", testingPeriod = 5)

Arguments

Value

sts

See Also

stsSD plotSD

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)

Clean the Atlas export dataframe

Description

Clean the Atlas data export dataframe before signal detection analysis
(see importAtlasExport and online ECDC Atlas: http://atlas.ecdc.europa.eu/public/index.aspx)

Usage

cleanAtlasExport(x)

Arguments

Details

The function will:

• Filter only on case based indicators i.e. 'Reported Cases"

• Create four additional time variables to ease the analysis:
  TimeUnit ('Year', 'Month', 'Week'),
  TimeYear (xxxx),
  TimeMonth (xx)
  TimeWeek(xx)

• Keep only variables of interest i.e. "HealthTopic", "Population", "Time", "RegionName", "NumValue"

Value

dataframe

See Also

importAtlasExport filterAtlasExport

Examples

## Not run: 
dataset <- cleanAtlasExport( importAtlasExport(x = 'ECDC_surveillance_data_Anthrax.csv') )

## End(Not run)

Filter clean Atlas export

Description

Filter clean Atlas export according to input parameters

Usage

filterAtlasExport(x, input, stratified)

Arguments

Value

dataframe filtered on the selected parameters (input list)

See Also

cleanAtlasExport aggAtlasExport

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)

Import ECDC Atlas export file (csv)

Description

Import ECDC Atlas csv export file
(exported from the online ECDC Atlas: http://atlas.ecdc.europa.eu/public/index.aspx)
e.g. "ECDC_surveillance_data_Anthrax.csv"

Usage

importAtlasExport(x)

Arguments

Details

The function will interpret missing reports '-' as NA values

Value

dataframe

See Also

cleanAtlasExport

Examples

## Not run: 
dataset <- importAtlasExport(x = 'ECDC_surveillance_data_Anthrax.csv')

## End(Not run)

Plot the Signal Detection time series

Description

Plot the Signal Detection time series including historical data, alarm detection period and alarms

Usage

plotSD(x, input, subRegionName, x.sts, x.algo)

Arguments

Value

plot

See Also

SignalData runEpiSDApp

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)

Run the EpiSignalDectection 'shiny' application

Description

Run the 'shiny' interactive application for signal detection analysis using ECDC Atlas export data.

Usage

runEpiSDApp()

Details

Datasets to use in the tool:

• Default dataset included in the application (Salmonellosis 2007-2016 or Measles 1999-2018 data);

• External dataset using the "Browse" button in the application:

  • –> An export (csv format) from the ECDC Surveillance Atlas of Infectious Diseases: http://atlas.ecdc.europa.eu/public/index.aspx.

    On the ECDC "Surveillance Atlas of Infectious Diseases" web site:

    • 1- Choose the disease/health topic to analyse

    • 2- Export the data (csv) using the default settings

    • 3- Import the csv in the application

    • 4- You can now explore the disease time series for signal detection...

  • –> Any dataset specified as described in the package vignette.

Examples

## Not run: 
# --- Run the 'shiny' app
# --- (NB: please open the app in an external browser
# --- in order to facilitate its use)
runEpiSDApp()

## End(Not run)

Run the EpiSignalDetection report (HTML markdown)

Description

Function to render the markdown report of alarms in HTML format for ECDC Signal Detection Report

Usage

runEpiSDReport(input, stratified, outputfile, run_pandoc = TRUE)

Arguments

Details

Datasets to use in the report:

• Default dataset included in the package (Salmonellosis 2007-2016 or Measles 1999-2018 data) (i.e. input$file = NULL);

• External dataset:

  • –> An export (csv format) from the ECDC Surveillance Atlas of Infectious Diseases: http://atlas.ecdc.europa.eu/public/index.aspx.

    On the ECDC "Surveillance Atlas of Infectious Diseases" web site:

    • 1- Choose the disease/health topic to analyse

    • 2- Export the data (csv) using the default settings

    • 3- Specify the location of this external dataset in the input argument of the runEpiSDReport() function (e.g. input <- list(file = list(datapath = "C:/Users/Downloads/ECDC_surveillance_data_Pertussis.csv"), disease = "Pertussis", country = "Greece", indicator = "Reported cases", stratification = "All cases", unit = "Month", daterange = c("2011-12-01", "2016-12-01"), algo = "FarringtonFlexible", testingperiod = 3))

    • 4- You can now render the re markdown report... (e.g. runEpiSDReport(input = input))

  • –> Any dataset specified as described in the package vignette.

Value

An HTML report. When run_pandoc = TRUE, the compiled document is written into the output file, and the path of the output file is returned. When run_pandoc = FALSE, the path of the Markdown output file, with attributes knit_meta (the knitr meta data collected from code chunks) and intermediates (the intermediate files/directories generated by render())

See Also

Default dataset used in the report SignalData

Signal Detection Application runEpiSDApp

Examples

## Not run: 
#-- Running the report as a standalone function
runEpiSDReport()    #Definition of each input parameter
                     #is done one by one through the R console

#---> OR

#-- First setting the parameters to run the report for
input <- list(
disease = "Salmonellosis",
country = "Portugal",
indicator = "Reported cases",
stratification = "Confirmed cases",
unit = "Month",
daterange = c("2011-01-01", "2016-12-31"),
algo = "FarringtonFlexible",
testingperiod = 6
)

#-- Second running the report based on the EpiSignalDetection::SignalData dataset
#-- and store it in a temporary folder
runEpiSDReport(input = input)

#-- Running the report based on the EpiSignalDetection::SignalData dataset
#-- and store the HTML output 'test.html' in the folder 'C:/R/'
runEpiSDReport(input = input, outputfile = "C:/R/test.html")

#-- Running the report based on external data
input <- list(
file = list(datapath = "C:/Users/Downloads/ECDC_surveillance_data_Pertussis.csv"),
disease = "Pertussis",
country = "Greece",
indicator = "Reported cases",
stratification = "All cases",
unit = "Month",
daterange = c("2011-12-01", "2016-12-01"),
algo = "FarringtonFlexible",
testingperiod = 3
)

runEpiSDReport(input = input, stratified = TRUE)

## End(Not run)

Build sts object

Description

Build sts surveillance object

Usage

stsSD(observedCases, studyPeriod, timeUnit = "Month", startYM = c(2000, 1) )

Arguments

Value

sts

See Also

aggAtlasExport algoSD

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"))))

Compute the study period

Description

Compute a dataframe including two types of dates corresponding to the study period defined in the list of parameters input
(i.e. StudyPeriod = approximated daily date; Time = exact date in the format according to the time unit parameter)

Usage

studyPeriod(input)

Arguments

Value

Dataframe including the complete time series with no gaps:

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)