# surveillance v1.12.1

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## Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

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 H�hle 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 H�hle (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>.

## Functions in surveillance

Name | Description | |

imdepi | Occurrence of Invasive Meningococcal Disease in Germany | |

plot.disProg | Plot Generation of the Observed and the defined Outbreak States of a (multivariate) time series | |

stK | Diggle et al (1995) K-function test for space-time clustering | |

epidataCS_update | Update method for "epidataCS" | |

sts-class | Class "sts" -- surveillance time series | |

R0 | Computes reproduction numbers from fitted models | |

LRCUSUM.runlength | Run length computation of a CUSUM detector | |

abattoir | Abattoir Data | |

algo.call | Query Transmission to Specified Surveillance Algorithm | |

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

anscombe.residuals | Compute Anscombe Residuals | |

untie | Randomly Break Ties in Data | |

aggregate.disProg | Aggregate the observed counts | |

epidata | Continuous-Time SIR Event History of a Fixed Population | |

formatPval | Pretty p-Value Formatting | |

twinstim_step | Stepwise Model Selection by AIC | |

calibrationTest | Calibration Test for Poisson or Negative Binomial Predictions | |

algo.farrington | Surveillance for a count data time series using the Farrington method. | |

animate | Generic animation of spatio-temporal objects | |

estimateGLRNbHook | Hook function for in-control mean estimation | |

compMatrix.writeTable | Latex Table Generation | |

boda | Surveillance for an univariate count data time series using the Bayesian Outbreak Detection Algorithm (BODA) described in Manitz and H�hle{Hoehle} (2013) | |

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

deleval | Surgical failures data | |

shadar | Salmonella Hadar cases in Germany 2001-2006 | |

hhh4_methods | Print, Summary and other Standard Methods for "hhh4" Objects | |

coeflist | List Coefficients by Model Component | |

readData | Reading of Disease Data | |

epidataCS_animate | Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidemic | |

checkResidualProcess | Check the residual process of a fitted twinSIR or twinstim | |

meanResponse | Calculate mean response needed in algo.hhh | |

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

enlargeData | Data Enlargement | |

correct53to52 | Data Correction from 53 to 52 weeks | |

epidata_summary | Summarizing an Epidemic | |

knox | Knox Test for Space-Time Interaction | |

multiplicity.Spatial | Count Number of Instances of Points | |

algo.rki | The system used at the RKI | |

twinSIR_profile | Profile Likelihood Computation and Confidence Intervals | |

arlCusum | Calculation of Average Run Length for discrete CUSUM schemes | |

twinSIR_cox | Identify Endemic Components in an Intensity Model | |

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

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

marks | Import from package spatstat | |

multiplicity | Import from package spatstat | |

momo | Danish 1994-2008 all cause mortality data for six age groups | |

sts_animate | Animated Maps and Time Series of Disease Incidence | |

m1 | RKI SurvStat Data | |

salmNewport | Salmonella Newport cases in Germany 2004-2013 | |

stsplot | Plot-Methods for Surveillance Time-Series Objects | |

unionSpatialPolygons | Compute the Unary Union of "SpatialPolygons" | |

algo.quality | Computation of Quality Values for a Surveillance System Result | |

twinSIR_intensityplot | Plotting Paths of Infection Intensities for twinSIR Models | |

hhh4 | Fitting HHH Models with Random Effects and Neighbourhood Structure | |

twinSIR | Fit an Additive-Multiplicative Intensity Model for SIR Data | |

magic.dim | Returns a suitable k1 x k2 for plotting the disProgObj | |

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

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 | |

plot.hhh4 | Plots for Fitted hhh4-models | |

wrap.algo | Multivariate Surveillance through independent univariate algorithms | |

hhh4_predict | Predictions from a hhh4 Model | |

linelist2sts | Convert individual case information based on dates into an aggregated time series of counts | |

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

twinstim_update | update-method for "twinstim" | |

twinstim_iafplot | Plot the Spatial or Temporal Interaction Function of a twimstim | |

polyAtBorder | Indicate Polygons at the Border | |

simHHH | Simulates data based on the model proposed by Held et. al (2005) | |

test | Print xtable for several diseases and the summary | |

twinstim_epitest | Permutation Test for Space-Time Interaction in "twinstim" | |

twinstim_siaf | Spatial Interaction Function Objects | |

algo.cdc | The CDC Algorithm | |

discpoly | Polygonal Approximation of a Disc/Circle | |

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

stcd | Spatio-temporal cluster detection | |

algo.bayes | The Bayes System | |

algo.farrington.assign.weights | Assign weights to base counts | |

hepatitisA | Hepatitis A in Germany | |

twinstim_intensity | Plotting Intensities of Infection over Time or Space | |

glm_epidataCS | Fit an Endemic-Only twinstim as a Poisson-glm | |

findH | Find decision interval for given in-control ARL and reference value | |

hhh4_W | Power-Law and Nonparametric Neighbourhood Weights for hhh4-Models | |

findK | Find reference value | |

algo.farrington.threshold | Compute prediction interval for a new observation | |

algo.farrington.fitGLM | Fit the Poisson GLM of the Farrington procedure for a single time point | |

create.grid | Create a Matrix of Initial Values for algo.hhh.grid | |

epidataCS_aggregate | Conversion (aggregation) of "epidataCS" to "epidata" or "sts" | |

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

isoWeekYear | Find ISO week and ISO year of a vector of Date objects on Windows | |

hhh4_simulate_plot | Summarize Simulations from "hhh4" Models | |

makePlot | Plot Generation | |

nbOrder | Determine Neighbourhood Order Matrix from Binary Adjacency Matrix | |

runifdisc | Sample Points Uniformly on a Disc | |

isScalar | Checks if the Argument is Scalar | |

hhh4_update | update a fitted "hhh4" model | |

print.algoQV | Print quality value object | |

scale.gpc.poly | Centering and Scaling a "gpc.poly" Polygon | |

algo.outbreakP | Semiparametric surveillance of outbreaks | |

sim.seasonalNoise | Generation of Background Noise for Simulated Timeseries | |

create.disProg | Creating an object of class disProg | |

epidata_animate | Spatio-Temporal Animation of an Epidemic | |

epidataCS | Continuous Space-Time Marked Point Patterns with Grid-Based Covariates | |

meningo.age | Meningococcal infections in France 1985-1995 | |

farringtonFlexible | Surveillance for an univariate count data time series using the improved Farrington method described in Noufaily et al. (2012). | |

fluBYBW | Influenza in Southern Germany | |

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

plot.atwins | Plot results of a twins model fit | |

refvalIdxByDate | Compute indices of reference value using Date class | |

plot.survRes | Plot a survRes object | |

residuals.ah | Residuals from a HHH model | |

surveillance-package | [stage=build]{(meta | |

stsplot_spacetime | Map of Disease Incidence | |

toLatex.sts | toLatex-Method for "sts" Objects | |

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

stsNewport | Salmonella Newport cases in Germany 2004-2013 | |

algo.cusum | CUSUM method | |

algo.hhh | Fit a Classical HHH Model (DEPRECATED) | |

campyDE | Cases of Campylobacteriosis and Absolute Humidity in Germany 2002-2011 | |

epidata_plot | Plotting the Evolution of an Epidemic | |

addFormattedXAxis | Formatted Time Axis for "sts" Objects | |

ha | Hepatitis A in Berlin | |

algo.twins | Model fit based on a two-component epidemic model | |

earsC | Surveillance for a count data time series using the EARS C1, C2 or C3 method. | |

sts_observation | Function for creating a sts-object with a given observation date | |

epidataCS_plot | Plotting the Events of an Epidemic over Time and Space | |

hhh4_formula | Specify Formulae in a Random Effects HHH Model | |

zetaweights | Power-Law Weights According to Neighbourhood Order | |

hhh4_simulate | Simulate "hhh4" Count Time Series | |

hhh4_validation | Predictive Model Assessment for hhh4 Models | |

intersectPolyCircle | Intersection of a Polygonal and a Circular Domain | |

[,sts-methods | Extraction and Subsetting of "sts" Objects | |

stsplot_time | Time-Series Plots for "sts" Objects | |

sim.pointSource | Simulate Point-Source Epidemics | |

stsplot_space | Map of Disease Incidence During a Given Period | |

epidataCS_permute | Randomly Permute Time Points or Locations of "epidataCS" | |

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

primeFactors | Prime number factorization | |

qlomax | Quantile Function of the Lomax Distribution | |

loglikelihood | Calculation of the loglikelihood needed in algo.hhh | |

stsSlot-generics | Generic functions to access "sts" slots | |

sts_creation | Function for simulating a time series | |

twinSIR_simulation | Simulation of Epidemic Data | |

residualsCT | Extract Cox-Snell-like Residuals of a Fitted Point Process | |

twinstim_simulation | Simulation of a Self-Exciting Spatio-Temporal Point Process | |

MMRcoverageDE | MMR coverage levels in the 16 states of Germany | |

algo.hhh.grid | Fit a Classical HHH Model (DEPRECATED) with Varying Start Values | |

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

layout.labels | Layout Items for spplot | |

categoricalCUSUM | CUSUM detector for time-varying categorical time series | |

disProg2sts | Convert disProg object to sts and vice versa | |

hhh4_calibration | Test Calibration of a hhh4 Model | |

predict.ah | Predictions from a HHH model | |

measlesDE | Measles in the 16 states of Germany | |

salmHospitalized | Hospitalized Salmonella cases in Germany 2004-2014 | |

find.kh | Determine the k and h values in a standard normal setting | |

plapply | Verbose and Parallel lapply | |

toFileDisProg | Writing of Disease Data | |

siaf.simulatePC | Simulation from an Isotropic Spatial Kernel via Polar Coordinates | |

addSeason2formula | Function that adds a sine-/cosine formula to an existing formula. | |

algo.compare | Comparison of Specified Surveillance Systems using Quality Values | |

algo.glrnb | Count Data Regression Charts | |

bodaDelay | Bayesian Outbreak Detection in the Presence of Reporting Delays | |

algo.summary | Summary Table Generation for Several Disease Chains | |

epidata_intersperse | Impute Blocks for Extra Stops in "epidata" Objects | |

poly2adjmat | Derive Adjacency Structure of "SpatialPolygons" | |

twinSIR_exData | Toy Data for twinSIR | |

twinstim_plot | Plot methods for fitted twinstim's | |

twinstim_tiaf | Temporal Interaction Function Objects | |

all.equal | Test if Two Model Fits are (Nearly) Equal | |

permutationTest | Monte Carlo Permutation Test for Paired Individual Scores | |

xtable.algoQV | Xtable quality value object | |

intensityplot | Plot Paths of Point Process Intensities | |

pairedbinCUSUM | Paired binary CUSUM and its run-length computation | |

make.design | Create the design matrices | |

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

bestCombination | Partition of a number into two factors | |

ranef | Import from package nlme | |

twinSIR_methods | Print, Summary and Extraction Methods for "twinSIR" Objects | |

aggregate-methods | Aggregate an "sts" Object Over Time or Across Units | |

stsNC-class | Class "stsNC" -- a class inheriting from class sts which allows the user to store the results of back-projecting surveillance time series | |

twinstim_methods | Print, Summary and Extraction Methods for "twinstim" Objects | |

twinstim_iaf | Temporal and Spatial Interaction Functions for twinstim | |

twinstim_profile | Profile Likelihood Computation and Confidence Intervals for twinstim objects | |

sumNeighbours | Calculates the sum of counts of adjacent areas | |

testSim | Print xtable for a Simulated Disease and the Summary | |

twinstim | Fit a Two-Component Spatio-Temporal Point Process Model | |

surveillance.options | Options of the surveillance Package | |

No Results! |

## Last month downloads

## Details

Date | 2016-05-18 |

LinkingTo | Rcpp |

License | GPL-2 |

URL | http://surveillance.r-forge.r-project.org/ |

Additional_repositories | https://www.math.ntnu.no/inla/R/stable |

BugReports | https://r-forge.r-project.org/tracker/?group_id=45 |

Encoding | latin1 |

VignetteBuilder | utils, knitr |

NeedsCompilation | yes |

Packaged | 2016-05-17 22:48:20 UTC; sebastian |

Repository | CRAN |

Date/Publication | 2016-05-18 06:53:12 |

suggests | animation , coda , colorspace , gamlss , ggplot2 , gpclib , grid , gridExtra , gsl , INLA (>= 0.0-1458166556) , intervals , knitr , lattice , maptools , maxLik , memoise , MGLM , msm , numDeriv , parallel , polyclip , quadprog , rgeos , runjags , scales , spc , spdep , splancs , testthat (>= 0.11.0) , xts |

depends | base (>= 3.2.0) , graphics , grDevices , methods , polyCub (>= 0.4-3) , R (>= 3.2.0) , sp (>= 1.0-15) , stats , utils , xtable (>= 1.7-0) |

imports | MASS , Matrix , nlme , Rcpp (>= 0.11.0) , spatstat (>= 1.36-0) |

Contributors | Sebastian Meyer, Thais Correa, Mathias Hofmann, Christian Lang, Stefan Steiner, Mikko Virtanen, Wei Wei, Andrea Riebler, Michael Hhle, R Core team, Valentin Wimmer, Leonhard Held, Michaela Paul, Juliane Manitz, Daniel Saban�s Bov�, Ma�lle Salmon, Dirk Schumacher |

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