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