Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hhle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. 'hhh4' estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. 'twinSIR' models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hhle (2009) <doi:10.1002/bimj.200900050>. 'twinstim' estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2015) <http://arxiv.org/abs/1411.0416>.
|Author||Michael Hhle [aut, ths], Sebastian Meyer [aut, cre], Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Sabans Bov [ctb], Malle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)|
|Date of publication||2016-12-21 14:22:04|
|Maintainer||Sebastian Meyer <email@example.com>|
abattoir: Abattoir Data
addFormattedXAxis: Formatted Time Axis for '"sts"' Objects
addSeason2formula: Function that adds a sine-/cosine formula to an existing...
aggregate.disProg: Aggregate the observed counts
algo.bayes: The Bayes System
algo.call: Query Transmission to Specified Surveillance Algorithm
algo.cdc: The CDC Algorithm
algo.compare: Comparison of Specified Surveillance Systems using Quality...
algo.cusum: CUSUM method
algo.farrington: Surveillance for a count data time series using the...
algo.farrington.assign.weights: Assign weights to base counts
algo.farrington.fitGLM: Fit the Poisson GLM of the Farrington procedure for a single...
algo.farrington.threshold: Compute prediction interval for a new observation
algo.glrnb: Count Data Regression Charts
algo.hhh: Fit a Classical HHH Model (DEPRECATED)
algo.hhh.grid: Fit a Classical HHH Model (DEPRECATED) with Varying Start...
algo.hmm: Hidden Markov Model (HMM) method
algo.outbreakP: Semiparametric surveillance of outbreaks
algo.quality: Computation of Quality Values for a Surveillance System...
algo.rki: The system used at the RKI
algo.rogerson: Modified CUSUM method as proposed by Rogerson and Yamada...
algo.summary: Summary Table Generation for Several Disease Chains
algo.twins: Model fit based on a two-component epidemic model
all.equal: Test if Two Model Fits are (Nearly) Equal
animate: Generic animation of spatio-temporal objects
anscombe.residuals: Compute Anscombe Residuals
arlCusum: Calculation of Average Run Length for discrete CUSUM schemes
backprojNP: Non-parametric back-projection of incidence cases to exposure...
bestCombination: Partition of a number into two factors
boda: Surveillance for an univariate count data time series using...
bodaDelay: Bayesian Outbreak Detection in the Presence of Reporting...
calibration: Calibration Test for Poisson or Negative Binomial Predictions
campyDE: Cases of Campylobacteriosis and Absolute Humidity in Germany...
categoricalCUSUM: CUSUM detector for time-varying categorical time series
checkResidualProcess: Check the residual process of a fitted 'twinSIR' or...
coeflist: List Coefficients by Model Component
compMatrix.writeTable: Latex Table Generation
correct53to52: Data Correction from 53 to 52 weeks
create.disProg: Creating an object of class disProg
create.grid: Create a Matrix of Initial Values for 'algo.hhh.grid'
deleval: Surgical failures data
discpoly: Polygonal Approximation of a Disc/Circle
disProg2sts: Convert disProg object to sts and vice versa
earsC: Surveillance for a count data time series using the EARS C1,...
enlargeData: Data Enlargement
epidata: Continuous-Time SIR Event History of a Fixed Population
epidata_animate: Spatio-Temporal Animation of an Epidemic
epidataCS: Continuous Space-Time Marked Point Patterns with Grid-Based...
epidataCS_aggregate: Conversion (aggregation) of '"epidataCS"' to '"epidata"' or...
epidataCS_animate: Spatio-Temporal Animation of a Continuous-Time...
epidataCS_permute: Randomly Permute Time Points or Locations of '"epidataCS"'
epidataCS_plot: Plotting the Events of an Epidemic over Time and Space
epidataCS_update: Update method for '"epidataCS"'
epidata_intersperse: Impute Blocks for Extra Stops in '"epidata"' Objects
epidata_plot: Plotting the Evolution of an Epidemic
epidata_summary: Summarizing an Epidemic
estimateGLRNbHook: Hook function for in-control mean estimation
farringtonFlexible: Surveillance for an univariate count data time series using...
findH: Find decision interval for given in-control ARL and reference...
findK: Find reference value
find.kh: Determine the k and h values in a standard normal setting
fluBYBW: Influenza in Southern Germany
formatPval: Pretty p-Value Formatting
glm_epidataCS: Fit an Endemic-Only 'twinstim' as a Poisson-'glm'
ha: Hepatitis A in Berlin
hagelloch: 1861 Measles Epidemic in the City of Hagelloch, Germany
hepatitisA: Hepatitis A in Germany
hhh4: Fitting HHH Models with Random Effects and Neighbourhood...
hhh4_calibration: Test Calibration of a 'hhh4' Model
hhh4_formula: Specify Formulae in a Random Effects HHH Model
hhh4_methods: Print, Summary and other Standard Methods for '"hhh4"'...
hhh4_plot: Plots for Fitted 'hhh4'-models
hhh4_predict: Predictions from a 'hhh4' Model
hhh4_simulate: Simulate '"hhh4"' Count Time Series
hhh4_simulate_plot: Summarize Simulations from '"hhh4"' Models
hhh4_update: 'update' a fitted '"hhh4"' model
hhh4_validation: Predictive Model Assessment for 'hhh4' Models
hhh4_W: Power-Law and Nonparametric Neighbourhood Weights for...
husO104Hosp: Hospitalization date for HUS cases of the STEC outbreak in...
imdepi: Occurrence of Invasive Meningococcal Disease in Germany
influMen: Influenza and meningococcal infections in Germany, 2001-2006
inside.gpc.poly: Test Whether Points are Inside a '"gpc.poly"' Polygon
intensityplot: Plot Paths of Point Process Intensities
intersectPolyCircle: Intersection of a Polygonal and a Circular Domain
isoWeekYear: Find ISO week and ISO year of a vector of Date objects on...
isScalar: Checks if the Argument is Scalar
knox: Knox Test for Space-Time Interaction
ks.plot.unif: Plot the ECDF of a uniform sample with Kolmogorov-Smirnov...
layout.labels: Layout Items for 'spplot'
linelist2sts: Convert individual case information based on dates into an...
loglikelihood: Calculation of the loglikelihood needed in algo.hhh
LRCUSUM.runlength: Run length computation of a CUSUM detector
m1: RKI SurvStat Data
magic.dim: Returns a suitable k1 x k2 for plotting the disProgObj
make.design: Create the design matrices
makePlot: Plot Generation
marks: Import from package 'spatstat'
meanResponse: Calculate mean response needed in algo.hhh
measlesDE: Measles in the 16 states of Germany
measles.weser: Measles in the Weser-Ems region of Lower Saxony, Germany,...
meningo.age: Meningococcal infections in France 1985-1995
MMRcoverageDE: MMR coverage levels in the 16 states of Germany
momo: Danish 1994-2008 all cause mortality data for six age groups
multiplicity: Import from package 'spatstat'
multiplicity.Spatial: Count Number of Instances of Points
nbOrder: Determine Neighbourhood Order Matrix from Binary Adjacency...
nowcast: Adjust a univariate time series of counts for observed...
pairedbinCUSUM: Paired binary CUSUM and its run-length computation
permutationTest: Monte Carlo Permutation Test for Paired Individual Scores
pit: Non-Randomized Version of the PIT Histogram (for Count Data)
plapply: Verbose and Parallel 'lapply'
plot.atwins: Plot results of a twins model fit
plot.disProg: Plot Generation of the Observed and the defined Outbreak...
plot.survRes: Plot a survRes object
poly2adjmat: Derive Adjacency Structure of '"SpatialPolygons"'
polyAtBorder: Indicate Polygons at the Border
predict.ah: Predictions from a HHH model
primeFactors: Prime number factorization
print.algoQV: Print quality value object
qlomax: Quantile Function of the Lomax Distribution
R0: Computes reproduction numbers from fitted models
ranef: Import from package 'nlme'
readData: Reading of Disease Data
refvalIdxByDate: Compute indices of reference value using Date class
residuals.ah: Residuals from a HHH model
residualsCT: Extract Cox-Snell-like Residuals of a Fitted Point Process
rotaBB: Rotavirus cases in Brandenburg, Germany, during 2002-2013...
runifdisc: Sample Points Uniformly on a Disc
salmAllOnset: Salmonella cases in Germany 2001-2014 by data of symptoms...
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
scale.gpc.poly: Centering and Scaling a '"gpc.poly"' Polygon
shadar: Salmonella Hadar cases in Germany 2001-2006
simHHH: Simulates data based on the model proposed by Held et. al...
sim.pointSource: Simulate Point-Source Epidemics
sim.seasonalNoise: Generation of Background Noise for Simulated Timeseries
stcd: Spatio-temporal cluster detection
stK: Diggle et al (1995) K-function test for space-time clustering
stsAggregate: Aggregate an '"sts"' Object Over Time or Across Units
sts_animate: Animated Maps and Time Series of Disease Counts or Incidence
stsBP-class: Class "stsBP" - a class inheriting from class 'sts' which...
sts-class: Class '"sts"' - surveillance time series
sts_creation: Function for simulating a time series
stsNC-class: Class "stsNC" - a class inheriting from class 'sts' which...
stsNClist_animate: Animate a sequence of nowcasts
stsNewport: Salmonella Newport cases in Germany 2004-2013
sts_observation: Function for creating a sts-object with a given observation...
stsplot: Plot-Methods for Surveillance Time-Series Objects
stsplot_space: Map of Disease Counts/Incidence accumulated over a Given...
stsplot_spacetime: Map of Disease Incidence
stsplot_time: Time-Series Plots for '"sts"' Objects
stsSlots: Generic functions to access '"sts"' slots
stsXtrct: Extraction and Subsetting of '"sts"' Objects
sumNeighbours: Calculates the sum of counts of adjacent areas
surveillance.options: Options of the 'surveillance' Package
test: Print xtable for several diseases and the summary
testSim: Print xtable for a Simulated Disease and the Summary
toFileDisProg: Writing of Disease Data
toLatex.sts: 'toLatex'-Method for '"sts"' Objects
twinSIR: Fit an Additive-Multiplicative Intensity Model for SIR Data
twinSIR_cox: Identify Endemic Components in an Intensity Model
twinSIR_exData: Toy Data for 'twinSIR'
twinSIR_intensityplot: Plotting Paths of Infection Intensities for 'twinSIR' Models
twinSIR_methods: Print, Summary and Extraction Methods for '"twinSIR"' Objects
twinSIR_profile: Profile Likelihood Computation and Confidence Intervals
twinSIR_simulation: Simulation of Epidemic Data
twinstim: Fit a Two-Component Spatio-Temporal Point Process Model
twinstim_epitest: Permutation Test for Space-Time Interaction in '"twinstim"'
twinstim_iaf: Temporal and Spatial Interaction Functions for 'twinstim'
twinstim_iafplot: Plot the Spatial or Temporal Interaction Function of a...
twinstim_intensity: Plotting Intensities of Infection over Time or Space
twinstim_methods: Print, Summary and Extraction Methods for '"twinstim"'...
twinstim_plot: Plot methods for fitted 'twinstim"s
twinstim_profile: Profile Likelihood Computation and Confidence Intervals for...
twinstim_siaf: Spatial Interaction Function Objects
twinstim_siaf_simulatePC: Simulation from an Isotropic Spatial Kernel via Polar...
twinstim_simulation: Simulation of a Self-Exciting Spatio-Temporal Point Process
twinstim_step: Stepwise Model Selection by AIC
twinstim_tiaf: Temporal Interaction Function Objects
twinstim_update: 'update'-method for '"twinstim"'
unionSpatialPolygons: Compute the Unary Union of '"SpatialPolygons"'
untie: Randomly Break Ties in Data
wrap.algo: Multivariate Surveillance through independent univariate...
xtable.algoQV: Xtable quality value object
zetaweights: Power-Law Weights According to Neighbourhood Order