Package index • surveillance

surveillance-package surveillance: surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Data class “sts”

disProg2sts() sts2disProg(): Convert disProg object to sts and vice versa

linelist2sts(): Convert Dates of Individual Case Reports into a Time Series of Counts

sts(): Class "sts" – surveillance time series

aggregate(<sts>): Aggregate an "sts" Object Over Time or Across Units

stsBP-class coerce,sts,stsBP-method: Class "stsBP" – a class inheriting from class sts which allows the user to store the results of back-projecting or nowcasting surveillance time series

stsNC-class reportingTriangle reportingTriangle,stsNC-method delayCDF delayCDF,stsNC-method score score,stsNC-method predint predint,stsNC-method coerce,sts,stsNC-method: Class "stsNC" – a class inheriting from class sts which allows the user to store the results of back-projecting surveillance time series

animate_nowcasts(): Animate a Sequence of Nowcasts

alarms alarms upperbound upperbound control control epoch epoch observed observed population population multinomialTS multinomialTS neighbourhood neighbourhood: Generic Functions to Access "sts" Slots

`[`(<sts>): Subsetting "sts" Objects

animate(<sts>): Animated Maps and Time Series of Disease Counts or Incidence

autoplot.sts(): Time-Series Plots for "sts" Objects Using ggplot2

tidy.sts(): Convert an "sts" Object to a Data Frame in Long (Tidy) Format

plot(<sts>,<missing>): Plot Methods for Surveillance Time-Series Objects

stsplot_space(): Map of Disease Counts/Incidence accumulated over a Given Period

stsplot_time() stsplot_time1() stsplot_alarm(): Time-Series Plots for "sts" Objects

toLatex(<sts>): toLatex-Method for "sts" Objects

Prospective outbreak detection

algo.bayesLatestTimepoint() algo.bayes() algo.bayes1() algo.bayes2() algo.bayes3(): The Bayes System

algo.call(): Query Transmission to Specified Surveillance Algorithm

algo.cdcLatestTimepoint() algo.cdc(): The CDC Algorithm

algo.compare(): Comparison of Specified Surveillance Systems using Quality Values

algo.cusum(): CUSUM method

algo.farrington() farrington(): Surveillance for Count Time Series Using the Classic Farrington Method

algo.farrington.assign.weights(): Assign weights to base counts

algo.farrington.fitGLM() algo.farrington.fitGLM.fast() algo.farrington.fitGLM.populationOffset(): Fit Poisson GLM of the Farrington procedure for a single time point

algo.farrington.threshold(): Compute prediction interval for a new observation

algo.glrnb() algo.glrpois(): Count Data Regression Charts

algo.hmm(): Hidden Markov Model (HMM) method

algo.outbreakP(): Semiparametric surveillance of outbreaks

algo.quality(): Computation of Quality Values for a Surveillance System Result

algo.rkiLatestTimepoint() algo.rki() algo.rki1() algo.rki2() algo.rki3(): The system used at the RKI

algo.rogerson(): Modified CUSUM method as proposed by Rogerson and Yamada (2004)

algo.summary(): Summary Table Generation for Several Disease Chains

print(<algoQV>): Print Quality Value Object

wrap.algo() bayes() rki() cusum() glrpois() glrnb() outbreakP(): Multivariate Surveillance through independent univariate algorithms

boda(): Bayesian Outbreak Detection Algorithm (BODA)

LRCUSUM.runlength(): Run length computation of a CUSUM detector

categoricalCUSUM(): CUSUM detector for time-varying categorical time series

pairedbinCUSUM() pairedbinCUSUM.runlength(): Paired binary CUSUM and its run-length computation

arlCusum(): Calculation of Average Run Length for discrete CUSUM schemes

find.kh(): Determine the k and h values in a standard normal setting

findH() hValues(): Find decision interval for given in-control ARL and reference value

findK(): Find Reference Value

earsC(): Surveillance for a count data time series using the EARS C1, C2 or C3 method and its extensions

farringtonFlexible(): Surveillance for Univariate Count Time Series Using an Improved Farrington Method

stcd(): Spatio-temporal cluster detection

sim.pointSource(): Simulate Point-Source Epidemics

sim.seasonalNoise(): Generation of Background Noise for Simulated Timeseries

sts_creation(): Simulate Count Time Series with Outbreaks

refvalIdxByDate(): Compute indices of reference value using Date class

Modeling reporting delays

backprojNP(): Non-parametric back-projection of incidence cases to exposure cases using a known incubation time as in Becker et al (1991)

nowcast(): Adjust a univariate time series of counts for observed but-not-yet-reported events

bodaDelay(): Bayesian Outbreak Detection in the Presence of Reporting Delays

sts_observation(): Create an sts object with a given observation date

hhh4: endemic-epidemic time series of counts

hhh4(): Fitting HHH Models with Random Effects and Neighbourhood Structure

W_powerlaw() W_np(): Power-Law and Nonparametric Neighbourhood Weights for hhh4-Models

getNEweights() coefW(): Extract Neighbourhood Weights from a Fitted hhh4 Model

fe() ri(): Specify Formulae in a Random Effects HHH Model

print(<hhh4>) summary(<hhh4>) coef(<hhh4>) fixef(<hhh4>) ranef(<hhh4>) coeflist(<hhh4>) formula(<hhh4>) nobs(<hhh4>) logLik(<hhh4>) vcov(<hhh4>) confint(<hhh4>) residuals(<hhh4>): Print, Summary and other Standard Methods for "hhh4" Objects

plot(<hhh4>) plotHHH4_fitted() plotHHH4_fitted1() plotHHH4_season() getMaxEV_season() plotHHH4_maxEV() getMaxEV() plotHHH4_maps() plotHHH4_ri() plotHHH4_neweights(): Plots for Fitted hhh4-models

predict(<hhh4>): Predictions from a hhh4 Model

simulate(<hhh4>): Simulate "hhh4" Count Time Series

plot(<hhh4sims>) aggregate(<hhh4sims>) as.hhh4simslist() plot(<hhh4simslist>) aggregate(<hhh4simslist>) plotHHH4sims_size() plotHHH4sims_time() plotHHH4sims_fan(): Plot Simulations from "hhh4" Models

scores(<hhh4sims>) scores(<hhh4simslist>): Proper Scoring Rules for Simulations from hhh4 Models

update(<hhh4>): update a fitted "hhh4" model

oneStepAhead() quantile(<oneStepAhead>) confint(<oneStepAhead>) plot(<oneStepAhead>) scores(<oneStepAhead>) calibrationTest(<oneStepAhead>) pit(<oneStepAhead>) scores(<hhh4>) calibrationTest(<hhh4>) pit(<hhh4>): Predictive Model Assessment for hhh4 Models

addSeason2formula(): Add Harmonics to an Existing Formula

calibrationTest(): Calibration Tests for Poisson or Negative Binomial Predictions

scores() logs() rps() dss() ses(): Proper Scoring Rules for Poisson or Negative Binomial Predictions

makeControl(): Generate control Settings for an hhh4 Model

twinstim: endemic-epidemic spatio-temporal point process

as.epidataCS() print(<epidataCS>) nobs(<epidataCS>) head(<epidataCS>) tail(<epidataCS>) `[`(<epidataCS>) subset(<epidataCS>) marks(<epidataCS>) summary(<epidataCS>) print(<summary.epidataCS>) as.stepfun(<epidataCS>) getSourceDists(): Continuous Space-Time Marked Point Patterns with Grid-Based Covariates

epidataCS2sts() as.epidata(<epidataCS>): Conversion (aggregation) of "epidataCS" to "epidata" or "sts"

animate(<epidataCS>): Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidemic

permute.epidataCS(): Randomly Permute Time Points or Locations of "epidataCS"

plot(<epidataCS>) epidataCSplot_time() epidataCSplot_space(): Plotting the Events of an Epidemic over Time and Space

update(<epidataCS>): Update method for "epidataCS"

glm_epidataCS(): Fit an Endemic-Only twinstim as a Poisson-glm

untie(): Randomly Break Ties in Data

R0() simpleR0(): Computes reproduction numbers from fitted models

residuals(<twinSIR>) residuals(<twinstim>) residuals(<simEpidataCS>): Extract Cox-Snell-like Residuals of a Fitted Point Process

twinstim(): Fit a Two-Component Spatio-Temporal Point Process Model

epitest() coef(<epitest>) plot(<epitest>): Permutation Test for Space-Time Interaction in "twinstim"

siaf.constant() siaf.step() siaf.gaussian() siaf.exponential() siaf.powerlaw() siaf.powerlaw1() siaf.powerlawL() siaf.student() tiaf.constant() tiaf.step() tiaf.exponential(): Temporal and Spatial Interaction Functions for twinstim

iafplot(): Plot the Spatial or Temporal Interaction Function of a twimstim

intensityplot(<twinstim>) intensityplot(<simEpidataCS>) intensity.twinstim(): Plotting Intensities of Infection over Time or Space

print(<twinstim>) summary(<twinstim>) coeflist(<twinstim>) vcov(<twinstim>) logLik(<twinstim>) nobs(<twinstim>) print(<summary.twinstim>) toLatex(<summary.twinstim>) xtable(<summary.twinstim>): Print, Summary and Extraction Methods for "twinstim" Objects

plot(<twinstim>): Plot methods for fitted twinstim's

profile(<twinstim>): Profile Likelihood Computation and Confidence Intervals for twinstim objects

siaf(): Spatial Interaction Function Objects

simEndemicEvents(): Quick Simulation from an Endemic-Only twinstim

simEpidataCS() simulate(<twinstim>): Simulation of a Self-Exciting Spatio-Temporal Point Process

stepComponent() add1(<twinstim>) drop1(<twinstim>): Stepwise Model Selection by AIC

tiaf(): Temporal Interaction Function Objects

update(<twinstim>): update-method for "twinstim"

knox() plot(<knox>): Knox Test for Space-Time Interaction

stKtest() plot(<stKtest>): Diggle et al (1995) K-function test for space-time clustering

checkResidualProcess(): Check the residual process of a fitted twinSIR or twinstim

twinSIR: multivariate temporal point process

as.epidata() print(<epidata>) `[`(<epidata>) update(<epidata>): Continuous-Time SIR Event History of a Fixed Population

animate(<summary.epidata>) animate(<epidata>): Spatio-Temporal Animation of an Epidemic

intersperse(): Impute Blocks for Extra Stops in "epidata" Objects

plot(<summary.epidata>) plot(<epidata>) stateplot(): Plotting the Evolution of an Epidemic

summary(<epidata>) print(<summary.epidata>): Summarizing an Epidemic

residuals(<twinSIR>) residuals(<twinstim>) residuals(<simEpidataCS>): Extract Cox-Snell-like Residuals of a Fitted Point Process

twinSIR(): Fit an Additive-Multiplicative Intensity Model for SIR Data

plot(<twinSIR>) intensityplot(<twinSIR>) intensityplot(<simEpidata>): Plotting Paths of Infection Intensities for twinSIR Models

print(<twinSIR>) summary(<twinSIR>) AIC(<twinSIR>) extractAIC(<twinSIR>) vcov(<twinSIR>) logLik(<twinSIR>) print(<summary.twinSIR>): Print, Summary and Extraction Methods for "twinSIR" Objects

profile(<twinSIR>): Profile Likelihood Computation and Confidence Intervals

simEpidata() simulate(<twinSIR>): Simulation of Epidemic Data

checkResidualProcess(): Check the residual process of a fitted twinSIR or twinstim

Datasets

MMRcoverageDE: MMR coverage levels in the 16 states of Germany

abattoir: Abattoir Data

campyDE: Campylobacteriosis and Absolute Humidity in Germany 2002-2011

deleval: Surgical Failures Data

fluBYBW: Influenza in Southern Germany

ha ha.sts: Hepatitis A in Berlin

hagelloch: 1861 Measles Epidemic in the City of Hagelloch, Germany

hepatitisA: Hepatitis A in Germany

husO104Hosp: Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011

imdepi: Occurrence of Invasive Meningococcal Disease in Germany

imdepifit: Example twinstim Fit for the imdepi Data

influMen: Influenza and meningococcal infections in Germany, 2001-2006

m1: RKI SurvStat Data

measles.weser measlesWeserEms: Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002

measlesDE: Measles in the 16 states of Germany

meningo.age: Meningococcal infections in France 1985-1997

momo: Danish 1994-2008 all-cause mortality data for eight age groups

rotaBB: Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories

salmAllOnset: Salmonella cases in Germany 2001-2014 by data of symptoms onset

salmHospitalized: Hospitalized Salmonella cases in Germany 2004-2014

salmNewport: Salmonella Newport cases in Germany 2004-2013

salmonella.agona: Salmonella Agona cases in the UK 1990-1995

shadar: Salmonella Hadar cases in Germany 2001-2006

stsNewport: Salmonella Newport cases in Germany 2001-2015

Utilities

Plotting

animate(): Generic animation of spatio-temporal objects

intensityplot(): Plot Paths of Point Process Intensities

ks.plot.unif(): Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds

fanplot(): Fan Plot of Forecast Distributions

addFormattedXAxis(): Formatted Time Axis for "sts" Objects

layout.labels() layout.scalebar(): Layout Items for spplot

pit(): Non-Randomized Version of the PIT Histogram (for Count Data)

magic.dim(): Compute Suitable k1 x k2 Layout for Plotting

Spatial utilities

discpoly(): Polygonal Approximation of a Disc/Circle

poly2adjmat(): Derive Adjacency Structure of "SpatialPolygons"

polyAtBorder(): Indicate Polygons at the Border

intersectPolyCircle(): Intersection of a Polygonal and a Circular Domain

unionSpatialPolygons(): Compute the Unary Union of "SpatialPolygons"

marks: Import from package spatstat.geom

multiplicity: Import from package spatstat.geom

multiplicity(<Spatial>): Count Number of Instances of Points

nbOrder(): Determine Neighbourhood Order Matrix from Binary Adjacency Matrix

zetaweights(): Power-Law Weights According to Neighbourhood Order

Other utilities

all.equal(<twinstim>) all.equal(<hhh4>): Test if Two Model Fits are (Nearly) Equal

formatDate(): Convert Dates to Character (Including Quarter Strings)

formatPval(): Pretty p-Value Formatting

bestCombination(): Partition of a number into two factors

primeFactors(): Prime Number Factorization

clapply(): Conditional lapply

plapply(): Verbose and Parallel lapply

coeflist(): List Coefficients by Model Component

isoWeekYear(): Find ISO Week and Year of Date Objects

permutationTest(): Monte Carlo Permutation Test for Paired Individual Scores

surveillance.options() reset.surveillance.options(): Options of the surveillance Package

ranef fixef: Import from package nlme

anscombe.residuals(): Compute Anscombe Residuals