The CDC Algorithm
Surveillance for a count data time series using the Farrington method.
Modified CUSUM method as proposed by Rogerson and Yamada (2004)
Summarizing an Epidemic
Randomly Permute Time Points or Locations of "epidataCS"
The system used at the RKI
Power-Law and Nonparametric Neighbourhood Weights for hhh4
-Models
Predictions from a HHH model
Convert disProg object to sts and vice versa
CUSUM method
Function that adds a sine-/cosine formula to an existing formula.
Summary Table Generation for Several Disease Chains
Non-Randomized Version of the PIT Histogram (for Count Data)
algo.farrington.assign.weights
Assign weights to base counts
Continuous-Time SIR Event History of a Fixed Population
Salmonella Newport cases in Germany 2004-2013
Latex Table Generation
Checks if the Argument is Scalar
Count Data Regression Charts
Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidemic
Semiparametric surveillance of outbreaks
Plot Generation
Generic functions to access "sts"
slots
Creating an object of class disProg
Hepatitis A in Germany
Prime number factorization
List Coefficients by Model Component
Predictions from a hhh4
Model
Surgical failures data
The Bayes System
Data Enlargement
Continuous Space-Time Marked Point Patterns with Grid-Based Covariates
Layout Items for spplot
Calculation of the loglikelihood needed in algo.hhh
Find ISO week and ISO year of a vector of Date objects on Windows
Print, Summary and other Standard Methods for "hhh4"
Objects
Measles in the 16 states of Germany
Predictive Model Assessment for hhh4
Models
Spatio-temporal cluster detection
Plot results of a twins model fit
Plots for Fitted hhh4
-models
Print, Summary and Extraction Methods for "twinstim"
Objects
Surveillance for an univariate count data time series using the
Bayesian Outbreak Detection Algorithm (BODA) described in Manitz and
H�hle {Hoehle} (2013)
Calculate mean response needed in algo.hhh
Quantile Function of the Lomax Distribution
Hepatitis A in Berlin
Aggregate the the series of an sts object
Print quality value object
Generation of Background Noise for Simulated Timeseries
Animated Maps and Time Series of Disease Incidence
Calculation of Average Run Length for discrete CUSUM schemes
Permutation Test for Space-Time Interaction in "twinstim"
Plotting the Evolution of an Epidemic
Class "stsNC" -- a class inheriting from class sts
which
allows the user to store the results of back-projecting
surveillance time series
Impute Blocks for Extra Stops in "epidata"
Objects
Plot-Methods for Surveillance Time-Series Objects
Danish 1994-2008 all cause mortality data for six age groups
Plot methods for fitted twinstim
's
Fit an Endemic-Only twinstim
as a Poisson-glm
Extraction and Subsetting of sts objects Computes a matrix of initial values
Stepwise Model Selection by AIC
Surveillance for an univariate count data time series using the improved Farrington method described in Noufaily et al. (2012).
Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002
Intersection of a Polygonal and a Circular Domain
MMR coverage levels in the 16 states of Germany
Verbose and Parallel lapply
Map of Disease Incidence
Update method for "epidataCS"
Polygonal Approximation of a Disc/Circle
Randomly Break Ties in Data
RKI SurvStat Data
Model fit based on a two-component epidemic model
Paired binary CUSUM and its run-length computation
Simulates data based on the model proposed by Held et. al (2005)
Print xtable for a Simulated Disease and the Summary
Pretty p-Value Formatting
Generation of Simulated Point Source Epidemy
Identify Endemic Components in an Intensity Model
Fit the Poisson GLM of the Farrington procedure for a single
time point
Aggregate the observed counts
Profile Likelihood Computation and Confidence Intervals
Query Transmission to Specified Surveillance Algorithm
Non-parametric back-projection of incidence cases to exposure cases
using a known incubation time as in Becker et al (1991).
Determine Neighbourhood Order Matrix from Binary Adjacency Matrix
Hospitalized Salmonella cases in Germany 2004-2014
Import from package spatstat Create the design matrices
Adjust a univariate time series of counts for observed
but-not-yet-reported events
Import from package spatstat Extract Cox-Snell-like Residuals of a Fitted Point Process
Surveillance for a count data time series using the EARS C1, C2 or C3 method.
Plotting the Events of an Epidemic over Time and Space
Hidden Markov Model (HMM) method
Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories
Centering and Scaling a "gpc.poly"
Polygon
Abattoir Data
Knox Test for Space-Time Interaction
Compute Anscombe residuals
Computation of Quality Values for a Surveillance System Result
Class "sts"
-- surveillance time series
Find decision interval for given in-control ARL and reference value
Generic animation of spatio-temporal objects
Spatio-Temporal Animation of an Epidemic
Time-Series Plots for "sts"
Objects
Fit a Two-Component Spatio-Temporal Point Process Model
Cases of Campylobacteriosis and Absolute Humidity in Germany 2002-2011
Plot Paths of Point Process Intensities
algo.farrington.threshold
Compute prediction interval for a new observation
CUSUM detector for time-varying categorical time series
Fitting HHH Models with Random Effects and Neighbourhood Structure
1861 Measles Epidemic in the City of Hagelloch, Germany
Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds
Comparison of Specified Surveillance Systems using Quality Values
Count Number of Instances of Points
Reading of Disease Data
Returns a suitable k1 x k2 for plotting the disProgObj
Indicate Polygons at the Border
Salmonella Hadar cases in Germany 2001-2006
Class "stsBP" -- a class inheriting from class sts
which
allows the user to store the results of back-projecting or nowcasting
surveillance time series
Influenza and meningococcal infections in Germany, 2001-2006
Partition of a number into two factors
Data Correction from 53 to 52 weeks
update
a fitted "hhh4"
model
Writing of Disease Data
Plot the spatial or temporal interaction function of a twimstim
Simulation of Epidemic Data
Options of the surveillance Package Determine the k and h values in a standard normal setting
Run length computation of a CUSUM detector
Specify Formulae in a Random Effects HHH Model
Salmonella Newport cases in Germany 2001-2011 by data of symptoms onset
Function for simulating a time series
Influenza in Southern Germany
Monte Carlo Permutation Test for Paired Individual Scores
Bayesian aberration detection in presence of reporting delays.
Check the residual process of a fitted twinSIR
or twinstim
Occurrence of Invasive Meningococcal Disease in Germany
Model fit based on the Held, Hoehle, Hofman paper
Salmonella cases in Germany 2001-2014 by data of symptoms onset
Simulation from an Isotropic Spatial Kernel via Polar Coordinates
Residuals from a HHH model
Print xtable for several diseases and the summary
Compute indices of reference value using Date class
Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011
Map of Disease Incidence During a Given Period
Function to try multiple starting values
Hook function for in-control mean estimation
toLatex
-Method for (lists of) "sts"
Objects
Function for creating a sts-object with a given observation date
Salmonella Agona cases in the UK 1990-1995
Plotting Paths of Infection Intensities for twinSIR
Models
[stage=build]{(meta <- packageDescription("surveillance", encoding="latin1"))$Title}
[stage=build]{meta$Description}
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] Calculates the sum of counts of adjacent areas
Toy Data for twinSIR
Print, Summary and Extraction Methods for "twinSIR"
Objects
Spatial Interaction Function Objects
Profile Likelihood Computation and Confidence Intervals for
twinstim
objects
Convert individual case information based on dates into an aggregated
time series of counts
Conversion (aggregation) of "epidataCS"
to "epidata"
or "sts"
Derive Adjacency Structure of "SpatialPolygons"
Simulates data based on the model proposed by Paul and Held (2011)
Plot a survRes object
Sample Points Uniformly on a Disc
Plotting Intensities of Infection over Time or Space
Power-Law Weights According to Neighbourhood Order
Temporal and Spatial Interaction Functions for twinstim
Compute the Unary Union of "SpatialPolygons"
Multivariate Surveillance through independent univariate algorithms
Temporal Interaction Function Objects
Find reference value
Computes reproduction numbers from fitted models
Test Whether Points are Inside a "gpc.poly"
Polygon
Simulation of a Self-Exciting Spatio-Temporal Point Process
Hook function for in-control mean estimation
Poisson regression charts
Plot Generation of the Observed and the defined Outbreak States of a
(multivariate) time series
Diggle et al (1995) K-function test for space-time clustering
Fit an Additive-Multiplicative Intensity Model for SIR Data
Xtable quality value object
update
-method for "twinstim"