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surveillance (version 1.8-0)

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Description

A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g. discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for surveillance data, or continuous-space point process data such as the occurrence of disease or earthquakes. Main focus is on outbreak detection in count data time series originating from public health surveillance of infectious diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently the package contains implementations of typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Hoehle and Paul (2008). Furthermore, inference methods for the retrospective infectious disease model in Held et al (2005), Held et al (2006), Paul et al (2008) and Paul and Held (2011) are provided. A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in H�hle (2009) ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim", continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: The suggested package INLA is unfortunately not available from any mainstream repository - in case one wants to use the 'boda' algorithm one needs to manually install the INLA package as specified at http://www.r-inla.org/download.

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Version

Install

install.packages('surveillance')

Monthly Downloads

1,603

Version

1.8-0

License

GPL-2

Maintainer

Michael Hhle

Last Published

June 17th, 2014

Functions in surveillance (1.8-0)

algo.hhh

Model fit based on the Held, Hoehle, Hofman paper
measlesDE

Measles in the 16 states of Germany
loglikelihood

Calculation of the loglikelihood needed in algo.hhh
inside.gpc.poly

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

toLatex-Method for (lists of) "sts" Objects
runifdisc

Sample Points Uniformly on a Disc
disProg2sts

Convert disProg object to sts and vice versa
twinstim_profile

Profile Likelihood Computation and Confidence Intervals for twinstim objects
husO104Hosp

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

Hepatitis A in Berlin
predict.ah

Predictions from a HHH model
sim.seasonalNoise

Generation of Background Noise for Simulated Timeseries
multiplicity

Count Number of Instances of Points
residualsCT

Extract Cox-Snell-like Residuals of a Fitted Point Process
stsplot_space

Map of Disease Incidence During a Given Period
primeFactors

Prime number factorization
formatPval

Pretty p-Value Formatting
categoricalCUSUM

CUSUM detector for time-varying categorical time series
unionSpatialPolygons

Compute the Unary Union of "SpatialPolygons"
abattoir

Abattoir Data
R0

Computes basic reproduction numbers from fitted models
bestCombination

Partition of a number into two factors
ks.plot.unif

Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds
plot.disProg

Plot Generation of the Observed and the defined Outbreak States of a (multivariate) time series
algo.rki

The system used at the RKI
meanResponse

Calculate mean response needed in algo.hhh
magic.dim

Returns a suitable k1 x k2 for plotting the disProgObj
algo.farrington

Surveillance for a count data time series using the Farrington method.
enlargeData

Data Enlargement
make.design

Create the design matrices
pit

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

Fit an Endemic-Only twinstim as a Poisson-glm
refvalIdxByDate

Compute indices of reference value using Date class
m1

RKI SurvStat Data
scale.gpc.poly

Centering and Scaling a "gpc.poly" Polygon
isoWeekYear

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

Quantile Function of the Lomax Distribution
algo.cdc

The CDC Algorithm
twinSIR_epidata_animate

Spatio-Temporal Animation of an Epidemic
twinstim_methods

Print, Summary and Extraction Methods for "twinstim" Objects
find.kh

Determine the k and h values in a standard normal setting
imdepi

Occurrence of Invasive Meningococcal Disease in Germany
salmHospitalized

Hospitalized Salmonella cases in Germany 2004-2014
influMen

Influenza and meningococcal infections in Germany, 2001-2006
correct53to52

Data Correction from 53 to 52 weeks
algo.glrpois

Poisson regression charts
plot.survRes

Plot a survRes object
twinstim_tiaf

Temporal Interaction Function Objects
algo.quality

Computation of Quality Values for a Surveillance System Result
create.grid

Computes a matrix of initial values
twinstim_step

Stepwise Model Selection by AIC
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
twinSIR_epidata_intersperse

Impute Blocks for Extra Stops in "epidata" Objects
hagelloch

1861 measles epidemic in the city of Hagelloch, Germany
twinSIR_profile

Profile Likelihood Computation and Confidence Intervals
hhh4

Random effects HHH model fit as described in Paul and Held (2011)
algo.hmm

Hidden Markov Model (HMM) method
test

Print xtable for several diseases and the summary
poly2adjmat

Derive Adjacency Structure of "SpatialPolygons"
readData

Reading of Disease Data
twinSIR_intensityplot

Plotting Paths of Infection Intensities for twinSIR Models
isScalar

Checks if the Argument is Scalar
intersectPolyCircle

Intersection of a Polygonal and a Circular Domain
aggregate-methods

Aggregate the the series of an sts object
algo.farrington.assign.weights

Assign weights to base counts
fluBYBW

Influenza in Southern Germany
simHHH

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

Surgical failures data
untie

Randomly Break Ties in Data
discpoly

Polygonal Approximation of a Disc/Circle
plot.hhh4

Plots for Fitted hhh4-models
zetaweights

Power-Law Weights According to Neighbourhood Order
twinstim

Fit a Two-Component Spatio-Temporal Point Process Model
surveillance.options

Options of the surveillance Package
algo.bayes

The Bayes System
stsplot_time

Time-Series Plots for "sts" Objects
stcd

Spatio-temporal cluster detection
residuals.ah

Residuals from a HHH model
sumNeighbours

Calculates the sum of counts of adjacent areas
hhh4_formula

Specify Formulae in a Random Effects HHH Model
sts-class

Class "sts" -- surveillance time series
nowcast

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

Salmonella Newport cases in Germany 2004-2013
epidataCS_update

Update method for "epidataCS"
epidataCS

Continuous Space-Time Marked Point Patterns with Grid-Based Covariates
twinSIR_cox

Identify Endemic Components in an Intensity Model
animate

Generic animation of spatio-temporal objects
estimateGLRNbHook

Hook function for in-control mean estimation
anscombe.residuals

Compute Anscombe residuals
measles.weser

Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002
twinSIR_epidata_summary

Summarizing an Epidemic
twinSIR

Fit an Additive-Multiplicative Intensity Model for SIR Data
epidataCS_animate

Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidemic
algo.summary

Summary Table Generation for Several Disease Chains
algo.cusum

CUSUM method
findK

Find reference value
polyAtBorder

Indicate Polygons at the Border
intensityplot

Plot Paths of Point Process Intensities
hhh4_simulate

Simulates data based on the model proposed by Paul and Held (2011)
earsC

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

Plot results of a twins model fit
LRCUSUM.runlength

Run length computation of a CUSUM detector
testSim

Print xtable for a Simulated Disease and the Summary
twinSIR_epidata

Class for Epidemic Data Discrete in Space and Continuous in Time
twinSIR_methods

Print, Summary and Extraction Methods for "twinSIR" Objects
salmonella.agona

Salmonella Agona cases in the UK 1990-1995
wrap.algo

Multivariate Surveillance through independent univariate algorithms
twinstim_siaf

Spatial Interaction Function Objects
stsNC-class

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

Generic functions to access "sts" slots
xtable.algoQV

Xtable quality value object
findH

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

Simulation of Epidemic Data
estimateGLRPoisHook

Hook function for in-control mean estimation
momo

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

Salmonella Hadar cases in Germany 2001-2006
hhh4_methods

Print, Summary and other Standard Methods for "hhh4" Objects
[,sts-methods

Extraction and Subsetting of sts objects
stsplot

Plot-Methods for Surveillance Time-Series Objects
hhh4_W

Power-Law and Nonparametric Neighbourhood Weights for hhh4-Models
makePlot

Plot Generation
twinstim_iafplot

Plot the spatial or temporal interaction function of a twimstim
epidataCS_plot

Plotting the Events of an Epidemic over Time and Space
algo.farrington.threshold

Compute prediction interval for a new observation
hepatitisA

Hepatitis A in Germany
twinstim_intensity

Plotting Intensities of Infection over Time or Space
algo.hhh.grid

Function to try multiple starting values
checkResidualProcess

Check the residual process of a fitted twinSIR or twinstim
hhh4_validation

Predictive Model Assessment for hhh4 models
create.disProg

Creating an object of class disProg
algo.twins

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

Comparison of Specified Surveillance Systems using Quality Values
print.algoQV

Print quality value object
epidataCS_aggregate

Conversion (aggregation) of "epidataCS" to "epidata" or "sts"
twinstim_iaf

Temporal and Spatial Interaction Functions for twinstim
toFileDisProg

Writing of Disease Data
stsplot_spacetime

Map of Disease Incidence
aggregate.disProg

Aggregate the observed counts
campyDE

Cases of Campylobacteriosis and Absolute Humidity in Germany 2002-2011
rotaBB

Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories
addSeason2formula

Function that adds a sine-/cosine formula to an existing formula.
algo.call

Query Transmission to Specified Surveillance Algorithm
sim.pointSource

Generation of Simulated Point Source Epidemy
sts_animate

Animated Maps and Time Series of Disease Incidence
nbOrder

Determine Neighbourhood Order Matrix from Binary Adjacency Matrix
algo.farrington.fitGLM

Fit the Poisson GLM of the Farrington procedure for a single time point
farringtonFlexible

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

Monte Carlo Permutation Test for Paired Individual Scores
MMRcoverageDE

MMR coverage levels in the 16 states of Germany
algo.outbreakP

Semiparametric surveillance of outbreaks
linelist2sts

Convert individual case information based on dates into an aggregated time series
twinSIR_epidata_plot

Plotting the Evolution of an Epidemic
twinstim_simulation

Simulation of a Self-Exciting Spatio-Temporal Point Process
twinstim_plot

Plot methods for fitted twinstim's
backprojNP

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

Latex Table Generation
algo.glrnb

Count Data Regression Charts
boda

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

Modified CUSUM method as proposed by Rogerson and Yamada (2004)
arlCusum

Calculation of Average Run Length for discrete CUSUM schemes
surveillance-package

[stage=build]{(meta <- packageDescription("surveillance", encoding="latin1"))$Title} A package implementing statistical methods for the modelling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g. discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for surveillance data, or continuous-space point process data such as the occurrence of disease or earthquakes. Main focus is on outbreak detection in count data time series originating from public health surveillance of infectious diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently the package contains implementations of typical outbreak detection procedures such as Stroup et. al (1989), Farrington et al (1996), Rossi et al (1999), Rogerson and Yamada (2001), a Bayesian approach, negative binomial CUSUM methods and a detector based on generalized likelihood ratios. Furthermore, inference methods for the retrospective infectious disease model in Held et al (2005), Held et al (2006), Paul et al (2008) and Paul and Held (2011) are provided. A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in H�{oe}hle (2009) ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim", continuous space). The package contains several real-world datasets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion.
ll{ Package: [stage=build]{meta$Package} Version: [stage=build]{meta$Version} License: [stage=build]{meta$License} URL: http://surveillance.r-forge.r-project.org/ surveillance is an Rpackage implementing statistical methods for the retrospective modeling and prospective change-point detection in time series of counts, proportions and categorical data. The main application is in the detection of aberrations in routine collected public health data seen as univariate and multivariate time series of counts or point-processes. However, applications could just as well originate from environmetrics, econometrics or social sciences. As many methods rely on statistical process control methodology, the package is thus also relevant to quality control and reliability engineering. The fundamental data structure of the package is an S4 class sts wrapping observations, monitoring results and date handling for multivariate time series. Currently the package contains implementations typical outbreak detection procedures such as Stroup et al. (1989), Farrington et al., (1996), Rossi et al. (1999), Rogerson and Yamada (2001), a Bayesian approach (H�{oe}hle, 2007), negative binomial CUSUM methods (H�{oe}hle and Mazick, 2009), and a detector based on generalized likelihood ratios (H�{oe}hle and Paul, 2008). However, also CUSUMs for the prospective change-point detection in binomial, beta-binomial and multinomial time series is covered based on generalized linear modelling. This includes e.g. paired binary CUSUM described by Steiner et al. (1999) or paired comparison Bradley-Terry modelling described in H�{oe}hle (2010). The package contains several real-world datasets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Furthermore, the package contains inference methods for the retrospective infectious disease model in Held et al. (2005), Paul et al. (2008) ("algo.hhh") and Paul and Held (2011) ("hhh4") handling multivariate time series of counts. Furthermore, the fully Bayesian approach for univariate time series of counts from Held et al. (2006) ("twins") is also implemented. Self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in H�{oe}hle (2009) ("twinSIR") and Meyer et al (2012) ("twinstim"). Altogether, the package allows the modelling and monitoring of epidemic phenomena in temporal and spatio-temporal contexts.[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
twinstim_update

update-method for "twinstim"
pairedbinCUSUM

Paired binary CUSUM and its run-length computation
twinSIR_exData

Artificial data and data from the German Federal State Baden-Wuerttemberg
hhh4_predict

Predictions from a hhh4 Model