surveillance-package

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

Data class "sts"

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

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

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

stsSlot-generics

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_spacetime()

Map of Disease Incidence

stsplot_time() stsplot_time1() stsplot_alarm()

Time-Series Plots for "sts" Objects

toLatex(<sts>)

toLatex-Method for "sts" Objects

Old data class "disProg"

aggregate(<disProg>)

Aggregate the observed counts

create.disProg()

Creating an object of class disProg

disProg2sts() sts2disProg()

Convert disProg object to sts and vice versa

plot(<disProg>) plot.disProg.one()

Plot Generation of the Observed and the Defined Outbreak States of a (Multivariate) Time Series

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()

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() farrington() bayes() rki() cusum() glrpois() glrnb() outbreakP()

Multivariate Surveillance through independent univariate algorithms

xtable(<algoQV>)

Xtable quality value object

plot(<survRes>) plot.survRes.one()

Plot a survRes object

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()

Function that adds a sine-/cosine formula 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

abattoir

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 1985-1995

momo

Danish 1994-2008 all cause mortality data for six 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

inside.gpc.poly()

Test Whether Points are Inside a "gpc.poly" Polygon

intersectPolyCircle()

Intersection of a Polygonal and a Circular Domain

poly2adjmat()

Derive Adjacency Structure of "SpatialPolygons"

polyAtBorder()

Indicate Polygons at the Border

scale(<gpc.poly>)

Centering and Scaling a "gpc.poly" Polygon

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

Import from package nlme

anscombe.residuals()

Compute Anscombe Residuals

Deprecated functions

qlomax()

Deprecated Functions in Package surveillance

algo.twins()

Model fit based on a two-component epidemic model

plot(<atwins>)

Plot results of a twins model fit