Abattoir Data
Fit the Poisson GLM of the Farrington procedure for a single
time point
Impute Blocks for Extra Stops in "epidata"
Objects
Surgical failures data
Find decision interval for given in-control ARL and reference value
Fit a Classical HHH Model (DEPRECATED)
Summary Table Generation for Several Disease Chains
Fit a Two-Component Spatio-Temporal Point Process Model
Writing of Disease Data
Surveillance for a count data time series using the EARS C1, C2 or C3 method.
Test Calibration of a hhh4
Model
Poisson regression charts
Summarize Simulations from "hhh4"
Models
Plot Paths of Point Process Intensities
Semiparametric surveillance of outbreaks
Generation of Background Noise for Simulated Timeseries
Influenza in Southern Germany
Creating an object of class disProg
Randomly Break Ties in Data
Paired binary CUSUM and its run-length computation
The CDC Algorithm
Count Data Regression Charts
Randomly Permute Time Points or Locations of "epidataCS"
Power-Law and Nonparametric Neighbourhood Weights for hhh4
-Models
Hidden Markov Model (HMM) method
Calculation of Average Run Length for discrete CUSUM schemes
Query Transmission to Specified Surveillance Algorithm
Update method for "epidataCS"
Checks if the Argument is Scalar
Formatted Time Axis for "sts"
Objects
Comparison of Specified Surveillance Systems using Quality Values
Data Enlargement
Options of the surveillance Package Predictions from a hhh4
Model
Data Correction from 53 to 52 weeks
Calculates the sum of counts of adjacent areas
Measles in the 16 states of Germany
Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidemic
Intersection of a Polygonal and a Circular Domain
Calculation of the loglikelihood needed in algo.hhh
Reading of Disease Data
Continuous Space-Time Marked Point Patterns with Grid-Based Covariates
Summarizing an Epidemic
Animated Maps and Time Series of Disease Incidence
Extract Cox-Snell-like Residuals of a Fitted Point Process
Create the design matrices
Temporal and Spatial Interaction Functions for twinstim
update
a fitted "hhh4"
model
Quantile Function of the Lomax Distribution
Indicate Polygons at the Border
Simulation of a Self-Exciting Spatio-Temporal Point Process
Residuals from a HHH model
Modified CUSUM method as proposed by Rogerson and Yamada (2004)
Computation of Quality Values for a Surveillance System Result
Plotting Intensities of Infection over Time or Space
Create a Matrix of Initial Values for algo.hhh.grid
[stage=build]{(meta <- packageDescription("surveillance", encoding="latin1"))$Title}
The surveillance package implements statistical methods for the
retrospective modeling and prospective monitoring of epidemic phenomena
in temporal and spatio-temporal contexts.
Focus is on (routinely collected) public health surveillance data,
but the methods just as well apply to data from environmetrics,
econometrics or the social sciences. As many of the monitoring methods
rely on statistical process control methodology, the package is
also relevant to quality control and reliability engineering.
ll {
Package: [stage=build]{meta$Package}
Version: [stage=build]{meta$Version}
License: [stage=build]{meta$License}
URL: http://surveillance.r-forge.r-project.org/
The package implements many 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 modeling. This includes,
e.g., paired binary CUSUM described by Steiner et al. (1999) or paired
comparison Bradley-Terry modeling 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. In dealing with time
series data, the fundamental data structure of the package is the S4
class sts
wrapping observations, monitoring results and
date handling for multivariate time series.
A recent overview of the available monitoring procedures is
given by Salmon et al. (2016).
For the retrospective analysis of epidemic spread, the package
provides three endemic-epidemic modeling frameworks with
tools for visualization, likelihood inference, and simulation.
The function hhh4
offers inference methods for the
(multivariate) count time series models of Held et al. (2005), Paul et
al. (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and
Held (2014). See the vignette("hhh4")
for an introduction.
Furthermore, the fully Bayesian approach for univariate
time series of counts from Held et al. (2006) is implemented as
function algo.twins
.
Self-exciting point processes are modeled through endemic-epidemic
conditional intensity functions.
twinSIR
(H� {oe}hle, 2009) models the
susceptible-infectious-recovered (SIR) event history of a
fixed population, e.g, epidemics across farms or networks.
twinstim
(Meyer et al., 2012) fits spatio-temporal point
process models to point patterns of infective events, e.g.,
time-stamped geo-referenced surveillance data on infectious disease
occurrence.
A recent overview of the implemented space-time modeling frameworks
for epidemic phenomena is given by Meyer et al. (2016). [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],[object Object],[object Object] Hook function for in-control mean estimation
Simulation from an Isotropic Spatial Kernel via Polar Coordinates
Time-Series Plots for "sts"
Objects
Hook function for in-control mean estimation
Danish 1994-2008 all cause mortality data for six age groups
Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002
Plot the Spatial or Temporal Interaction Function of a twimstim
Spatial Interaction Function Objects
MMR coverage levels in the 16 states of Germany
CUSUM detector for time-varying categorical time series
Find reference value
Influenza and meningococcal infections in Germany, 2001-2006
Model fit based on a two-component epidemic model
Hepatitis A in Berlin
Latex Table Generation
Monte Carlo Permutation Test for Paired Individual Scores
Fit an Endemic-Only twinstim
as a Poisson-glm
Function for creating a sts-object with a given observation date
Layout Items for spplot
Toy Data for twinSIR
Prime number factorization
Class "stsNC" -- a class inheriting from class sts
which
allows the user to store the results of back-projecting
surveillance time series
Run length computation of a CUSUM detector
Salmonella Agona cases in the UK 1990-1995
Fit a Classical HHH Model (DEPRECATED) with Varying Start Values
1861 Measles Epidemic in the City of Hagelloch, Germany
Hepatitis A in Germany
Plot Generation of the Observed and the defined Outbreak States of a
(multivariate) time series
Salmonella Hadar cases in Germany 2001-2006
update
-method for "twinstim"
Identify Endemic Components in an Intensity Model
Stepwise Model Selection by AIC
Print quality value object
Surveillance for a count data time series using the Farrington method.
Surveillance for an univariate count data time series using the improved Farrington method described in Noufaily et al. (2012).
Fitting HHH Models with Random Effects and Neighbourhood Structure
Predictive Model Assessment for hhh4
Models
Print, Summary and Extraction Methods for "twinstim"
Objects
Determine Neighbourhood Order Matrix from Binary Adjacency Matrix
Permutation Test for Space-Time Interaction in "twinstim"
Print, Summary and Extraction Methods for "twinSIR"
Objects
Continuous-Time SIR Event History of a Fixed Population
Multivariate Surveillance through independent univariate algorithms
CUSUM method
Plots for Fitted hhh4
-models
Simulate "hhh4"
Count Time Series
RKI SurvStat Data
Derive Adjacency Structure of "SpatialPolygons"
Salmonella cases in Germany 2001-2014 by data of symptoms onset
Function for simulating a time series
Class "stsBP" -- a class inheriting from class sts
which
allows the user to store the results of back-projecting or nowcasting
surveillance time series
Print, Summary and other Standard Methods for "hhh4"
Objects
Cases of Campylobacteriosis and Absolute Humidity in Germany 2002-2011
Convert disProg object to sts and vice versa
Plotting the Evolution of an Epidemic
Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011
Plot results of a twins model fit
Power-Law Weights According to Neighbourhood Order
Aggregate the observed counts
Find ISO week and ISO year of a vector of Date objects on Windows
Import from package spatstat Print xtable for several diseases and the summary
Plot a survRes object
Plot methods for fitted twinstim
's
Conversion (aggregation) of "epidataCS"
to "epidata"
or "sts"
Partition of a number into two factors
Surveillance for an univariate count data time series using the
Bayesian Outbreak Detection Algorithm (BODA) described in Manitz and
H�hle {Hoehle} (2013)
Test Whether Points are Inside a "gpc.poly"
Polygon
Extraction and Subsetting of sts objects Print xtable for a Simulated Disease and the Summary
Profile Likelihood Computation and Confidence Intervals
Predictions from a HHH model
Function that adds a sine-/cosine formula to an existing formula.
Generic animation of spatio-temporal objects
The Bayes System
Meningococcal infections in France 1985-1995
Returns a suitable k1 x k2 for plotting the disProgObj
Salmonella Newport cases in Germany 2004-2013
Class "sts"
-- surveillance time series
Profile Likelihood Computation and Confidence Intervals for
twinstim
objects
Simulation of Epidemic Data
Computes reproduction numbers from fitted models
algo.farrington.threshold
Compute prediction interval for a new observation
Plotting the Events of an Epidemic over Time and Space
Calculate mean response needed in algo.hhh
Import from package spatstat Generic functions to access "sts"
slots
Fit an Additive-Multiplicative Intensity Model for SIR Data
Bayesian aberration detection in presence of reporting delays.
Pretty p-Value Formatting
Occurrence of Invasive Meningococcal Disease in Germany
Adjust a univariate time series of counts for observed
but-not-yet-reported events
Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories
Aggregate the the series of an sts object
Xtable quality value object
The system used at the RKI
Determine the k and h values in a standard normal setting
Generation of Simulated Point Source Epidemy
Hospitalized Salmonella cases in Germany 2004-2014
Plotting Paths of Infection Intensities for twinSIR
Models
Centering and Scaling a "gpc.poly"
Polygon
Non-parametric back-projection of incidence cases to exposure cases
using a known incubation time as in Becker et al (1991).
Compute Anscombe residuals
Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds
Plot Generation
Simulates data based on the model proposed by Held et. al (2005)
Map of Disease Incidence
algo.farrington.assign.weights
Assign weights to base counts
List Coefficients by Model Component
Polygonal Approximation of a Disc/Circle
Spatio-Temporal Animation of an Epidemic
Specify Formulae in a Random Effects HHH Model
Knox Test for Space-Time Interaction
Count Number of Instances of Points
Salmonella Newport cases in Germany 2004-2013
Plot-Methods for Surveillance Time-Series Objects
Map of Disease Incidence During a Given Period
Sample Points Uniformly on a Disc
toLatex
-Method for (lists of) "sts"
Objects
Temporal Interaction Function Objects
Compute the Unary Union of "SpatialPolygons"
Spatio-temporal cluster detection
Check the residual process of a fitted twinSIR
or twinstim
Diggle et al (1995) K-function test for space-time clustering
Calibration Test for Poisson or Negative Binomial Predictions
Non-Randomized Version of the PIT Histogram (for Count Data)
Compute indices of reference value using Date class
Convert individual case information based on dates into an aggregated
time series of counts
Verbose and Parallel lapply