Model fit based on the Held, Hoehle, Hofman paper
Measles in the 16 states of Germany
Calculation of the loglikelihood needed in algo.hhh
Test Whether Points are Inside a "gpc.poly" Polygon
toLatex-Method for (lists of) "sts" Objects
Sample Points Uniformly on a Disc
Convert disProg object to sts and vice versa
Profile Likelihood Computation and Confidence Intervals for
twinstim objects
Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011
Hepatitis A in Berlin
Predictions from a HHH model
Generation of Background Noise for Simulated Timeseries
Count Number of Instances of Points
Extract Cox-Snell-like Residuals of a Fitted Point Process
Map of Disease Incidence During a Given Period
Prime number factorization
Pretty p-Value Formatting
CUSUM detector for time-varying categorical time series
Compute the Unary Union of "SpatialPolygons"
Abattoir Data
Computes basic reproduction numbers from fitted models
Partition of a number into two factors
Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds
Plot Generation of the Observed and the defined Outbreak States of a
(multivariate) time series
The system used at the RKI
Calculate mean response needed in algo.hhh
Returns a suitable k1 x k2 for plotting the disProgObj
Surveillance for a count data time series using the Farrington method.
Data Enlargement
Create the design matrices
Non-Randomized Version of the PIT Histogram (for Count Data)
Fit an Endemic-Only twinstim as a Poisson-glm
Compute indices of reference value using Date class
RKI SurvStat Data
Centering and Scaling a "gpc.poly" Polygon
Find ISO week and ISO year of a vector of Date objects on Windows
Quantile Function of the Lomax Distribution
The CDC Algorithm
Spatio-Temporal Animation of an Epidemic
Print, Summary and Extraction Methods for "twinstim" Objects
Determine the k and h values in a standard normal setting
Occurrence of Invasive Meningococcal Disease in Germany
Hospitalized Salmonella cases in Germany 2004-2014
Influenza and meningococcal infections in Germany, 2001-2006
Data Correction from 53 to 52 weeks
Poisson regression charts
Plot a survRes object
Temporal Interaction Function Objects
Computation of Quality Values for a Surveillance System Result
Computes a matrix of initial values
Stepwise Model Selection by AIC
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
1861 measles epidemic in the city of Hagelloch, Germany
Profile Likelihood Computation and Confidence Intervals
Random effects HHH model fit as described in Paul and Held (2011)
Hidden Markov Model (HMM) method
Print xtable for several diseases and the summary
Derive Adjacency Structure of "SpatialPolygons"
Reading of Disease Data
Plotting Paths of Infection Intensities for twinSIR Models
Checks if the Argument is Scalar
Intersection of a Polygonal and a Circular Domain
Aggregate the the series of an sts object
algo.farrington.assign.weights
Assign weights to base counts
Influenza in Southern Germany
Simulates data based on the model proposed by Held et. al (2005)
Surgical failures data
Randomly Break Ties in Data
Polygonal Approximation of a Disc/Circle
Plots for Fitted hhh4-models
Power-Law Weights According to Neighbourhood Order
Fit a Two-Component Spatio-Temporal Point Process Model
Options of the surveillance Package The Bayes System
Time-Series Plots for "sts" Objects
Spatio-temporal cluster detection
Residuals from a HHH model
Calculates the sum of counts of adjacent areas
Specify Formulae in a Random Effects HHH Model
Class "sts" -- surveillance time series
Adjust a univariate time series of counts for observed
but-not-yet-reported events
Salmonella Newport cases in Germany 2004-2013
Update method for "epidataCS"
Continuous Space-Time Marked Point Patterns with Grid-Based Covariates
Identify Endemic Components in an Intensity Model
Generic animation of spatio-temporal objects
Hook function for in-control mean estimation
Compute Anscombe residuals
Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002
Summarizing an Epidemic
Fit an Additive-Multiplicative Intensity Model for SIR Data
Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidemic
Summary Table Generation for Several Disease Chains
CUSUM method
Find reference value
Indicate Polygons at the Border
Plot Paths of Point Process Intensities
Simulates data based on the model proposed by Paul and Held (2011)
Surveillance for a count data time series using the EARS C1, C2 or C3 method.
Plot results of a twins model fit
Run length computation of a CUSUM detector
Print xtable for a Simulated Disease and the Summary
Class for Epidemic Data Discrete in Space and Continuous in Time
Print, Summary and Extraction Methods for "twinSIR" Objects
Salmonella Agona cases in the UK 1990-1995
Multivariate Surveillance through independent univariate algorithms
Spatial Interaction Function Objects
Class "stsNC" -- a class inheriting from class sts which
allows the user to store the results of back-projecting
surveillance time series
Generic functions to access "sts" slots
Xtable quality value object
Find decision interval for given in-control ARL and reference value
Simulation of Epidemic Data
Hook function for in-control mean estimation
Danish 1994-2008 all cause mortality data for six age groups
Salmonella Hadar cases in Germany 2001-2006
Print, Summary and other Standard Methods for "hhh4" Objects
Extraction and Subsetting of sts objects Plot-Methods for Surveillance Time-Series Objects
Power-Law and Nonparametric Neighbourhood Weights for hhh4-Models
Plot Generation
Plot the spatial or temporal interaction function of a twimstim
Plotting the Events of an Epidemic over Time and Space
algo.farrington.threshold
Compute prediction interval for a new observation
Hepatitis A in Germany
Plotting Intensities of Infection over Time or Space
Function to try multiple starting values
Check the residual process of a fitted twinSIR or twinstim
Predictive Model Assessment for hhh4 models
Creating an object of class disProg
Model fit based on a two-component epidemic model
Comparison of Specified Surveillance Systems using Quality Values
Print quality value object
Conversion (aggregation) of "epidataCS" to "epidata" or "sts"
Temporal and Spatial Interaction Functions for twinstim
Writing of Disease Data
Map of Disease Incidence
Aggregate the observed counts
Cases of Campylobacteriosis and Absolute Humidity in Germany 2002-2011
Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories
Function that adds a sine-/cosine formula to an existing formula.
Query Transmission to Specified Surveillance Algorithm
Generation of Simulated Point Source Epidemy
Animated Maps and Time Series of Disease Incidence
Determine Neighbourhood Order Matrix from Binary Adjacency Matrix
Fit the Poisson GLM of the Farrington procedure for a single
time point
Surveillance for an univariate count data time series using the improved Farrington method described in Noufaily et al. (2012).
Monte Carlo Permutation Test for Paired Individual Scores
MMR coverage levels in the 16 states of Germany
Semiparametric surveillance of outbreaks
Convert individual case information based on dates into an aggregated time series
Plotting the Evolution of an Epidemic
Simulation of a Self-Exciting Spatio-Temporal Point Process
Plot methods for fitted twinstim's
Non-parametric back-projection of incidence cases to exposure cases
using a known incubation time as in Becker et al (1991).
Latex Table Generation
Count Data Regression Charts
Surveillance for an univariate count data time series using the
Bayesian Outbreak Detection Algorithm (BODA) described in Manitz and
H�hle {Hoehle} (2013)
Modified CUSUM method as proposed by Rogerson and Yamada (2004)
Calculation of Average Run Length for discrete CUSUM schemes
[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] update-method for "twinstim"
Paired binary CUSUM and its run-length computation
Artificial data and data from the German Federal State Baden-Wuerttemberg
Predictions from a hhh4 Model