Query Transmission to Specified Surveillance Algorithm
Formatted Time Axis for "sts"
Objects
Plot Paths of Point Process Intensities
Abattoir Data
List Coefficients by Model Component
Count Data Regression Charts
Layout Items for spplot
Cases of Campylobacteriosis and Absolute Humidity in Germany 2002-2011
CUSUM method
Determine the k and h values in a standard normal setting
Power-Law and Nonparametric Neighbourhood Weights for hhh4
-Models
Latex Table Generation
Test Calibration of a hhh4
Model
Calculation of the loglikelihood needed in algo.hhh
Compute Anscombe Residuals
Predictions from a HHH model
Hepatitis A in Berlin
Fit a Classical HHH Model (DEPRECATED)
Continuous Space-Time Marked Point Patterns with Grid-Based Covariates
Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011
Import from package spatstat Test if Two Model Fits are (Nearly) Equal
Simulate "hhh4"
Count Time Series
Plotting the Evolution of an Epidemic
Plots for Fitted hhh4
-models
Print, Summary and Extraction Methods for "twinSIR"
Objects
RKI SurvStat Data
Plot Generation of the Observed and the defined Outbreak States of a
(multivariate) time series
Permutation Test for Space-Time Interaction in "twinstim"
Fitting HHH Models with Random Effects and Neighbourhood Structure
Fit an Additive-Multiplicative Intensity Model for SIR Data
Simulation from an Isotropic Spatial Kernel via Polar Coordinates
Meningococcal infections in France 1985-1995
Predictions from a hhh4
Model
Writing of Disease Data
Create the design matrices
Generation of Background Noise for Simulated Timeseries
Summarize Simulations from "hhh4"
Models
Monte Carlo Permutation Test for Paired Individual Scores
update
a fitted "hhh4"
model
Measles in the 16 states of Germany
Influenza and meningococcal infections in Germany, 2001-2006
Knox Test for Space-Time Interaction
Simulates data based on the model proposed by Held et. al (2005)
Simulate Point-Source Epidemics
Options of the surveillance Package Calibration Test for Poisson or Negative Binomial Predictions
Class "stsNC" -- a class inheriting from class sts
which
allows the user to store the results of back-projecting
surveillance time series
[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 vignette("hhh4")
for a general introduction
and vignette("hhh4_spacetime")
for a discussion and
illustration of spatial hhh4
models.
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;
see vignette("twinSIR")
for an illustration.
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; see vignette("twinstim")
for an illustration.
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] Convert individual case information based on dates into an aggregated
time series of counts
Stepwise Model Selection by AIC
Simulation of a Self-Exciting Spatio-Temporal Point Process
Compute the Unary Union of "SpatialPolygons"
Plot Generation
Danish 1994-2008 all cause mortality data for six age groups
Indicate Polygons at the Border
Computes reproduction numbers from fitted models
The Bayes System
Surveillance for a count data time series using the Farrington method.
Fit a Classical HHH Model (DEPRECATED) with Varying Start Values
The system used at the RKI
Calculates the sum of counts of adjacent areas
Sample Points Uniformly on a Disc
Print xtable for a Simulated Disease and the Summary
Spatial Interaction Function Objects
Time-Series Plots for "sts"
Objects
Spatio-Temporal Animation of an Epidemic
Identify Endemic Components in an Intensity Model
Salmonella cases in Germany 2001-2014 by data of symptoms onset
Temporal and Spatial Interaction Functions for twinstim
Plot methods for fitted twinstim
's
Function that adds a sine-/cosine formula to an existing formula.
Polygonal Approximation of a Disc/Circle
Influenza in Southern Germany
Fit an Endemic-Only twinstim
as a Poisson-glm
Randomly Break Ties in Data
Find decision interval for given in-control ARL and reference value
Count Number of Instances of Points
Non-Randomized Version of the PIT Histogram (for Count Data)
Print quality value object
Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories
Salmonella Hadar cases in Germany 2001-2006
Function for creating a sts-object with a given observation date
Plot-Methods for Surveillance Time-Series Objects
Check the residual process of a fitted twinSIR
or twinstim
Function for simulating a time series
Simulation of Epidemic Data
Conversion (aggregation) of "epidataCS"
to "epidata"
or "sts"
Profile Likelihood Computation and Confidence Intervals
Plotting Paths of Infection Intensities for twinSIR
Models
Hook function for in-control mean estimation
Intersection of a Polygonal and a Circular Domain
Residuals from a HHH model
Salmonella Newport cases in Germany 2004-2013
Aggregate an "sts"
Object Over Time or Across Units
Model fit based on a two-component epidemic model
Surveillance for an univariate count data time series using the
Bayesian Outbreak Detection Algorithm (BODA) described in Manitz and
H�hle {Hoehle} (2013)
Aggregate the observed counts
Semiparametric surveillance of outbreaks
Bayesian Outbreak Detection in the Presence of Reporting Delays
Continuous-Time SIR Event History of a Fixed Population
CUSUM detector for time-varying categorical time series
Run length computation of a CUSUM detector
Surveillance for an univariate count data time series using the improved Farrington method described in Noufaily et al. (2012).
Randomly Permute Time Points or Locations of "epidataCS"
Returns a suitable k1 x k2 for plotting the disProgObj
Surgical failures data
Data Correction from 53 to 52 weeks
Plotting the Events of an Epidemic over Time and Space
Generic functions to access "sts"
slots
Centering and Scaling a "gpc.poly"
Polygon
Find ISO week and ISO year of a vector of Date objects on Windows
Toy Data for twinSIR
Verbose and Parallel lapply
Class "stsBP" -- a class inheriting from class sts
which
allows the user to store the results of back-projecting or nowcasting
surveillance time series
Plot a survRes object
Plot the Spatial or Temporal Interaction Function of a twimstim
Spatio-temporal cluster detection
Derive Adjacency Structure of "SpatialPolygons"
Update method for "epidataCS"
Summarizing an Epidemic
Predictive Model Assessment for hhh4
Models
Fit the Poisson GLM of the Farrington procedure for a single
time point
algo.farrington.threshold
Compute prediction interval for a new observation
Determine Neighbourhood Order Matrix from Binary Adjacency Matrix
Animated Maps and Time Series of Disease Incidence
Pretty p-Value Formatting
Calculation of Average Run Length for discrete CUSUM schemes
Convert disProg object to sts and vice versa
Test Whether Points are Inside a "gpc.poly"
Polygon
Impute Blocks for Extra Stops in "epidata"
Objects
Salmonella Agona cases in the UK 1990-1995
Diggle et al (1995) K-function test for space-time clustering
Computation of Quality Values for a Surveillance System Result
Power-Law Weights According to Neighbourhood Order
Modified CUSUM method as proposed by Rogerson and Yamada (2004)
Surveillance for a count data time series using the EARS C1, C2 or C3 method.
Plot results of a twins model fit
Reading of Disease Data
Print xtable for several diseases and the summary
Fit a Two-Component Spatio-Temporal Point Process Model
Plotting Intensities of Infection over Time or Space
update
-method for "twinstim"
Create a Matrix of Initial Values for algo.hhh.grid
1861 Measles Epidemic in the City of Hagelloch, Germany
Import from package spatstat Paired binary CUSUM and its run-length computation
Temporal Interaction Function Objects
Prime number factorization
Xtable quality value object
Multivariate Surveillance through independent univariate algorithms
Extract Cox-Snell-like Residuals of a Fitted Point Process
Map of Disease Incidence
Print, Summary and Extraction Methods for "twinstim"
Objects
Comparison of Specified Surveillance Systems using Quality Values
MMR coverage levels in the 16 states of Germany
Hidden Markov Model (HMM) method
The CDC Algorithm
Summary Table Generation for Several Disease Chains
Creating an object of class disProg
Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds
Adjust a univariate time series of counts for observed
but-not-yet-reported events
Profile Likelihood Computation and Confidence Intervals for
twinstim
objects
Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidemic
Specify Formulae in a Random Effects HHH Model
Compute indices of reference value using Date class
Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002
Map of Disease Incidence During a Given Period
toLatex
-Method for "sts"
Objects
algo.farrington.assign.weights
Assign weights to base counts
Non-parametric back-projection of incidence cases to exposure cases
using a known incubation time as in Becker et al (1991).
Partition of a number into two factors
Hepatitis A in Germany
Checks if the Argument is Scalar
Occurrence of Invasive Meningococcal Disease in Germany
Calculate mean response needed in algo.hhh
Hospitalized Salmonella cases in Germany 2004-2014
Class "sts"
-- surveillance time series
Salmonella Newport cases in Germany 2004-2013
Generic animation of spatio-temporal objects
Find reference value
Data Enlargement
Print, Summary and other Standard Methods for "hhh4"
Objects
Quantile Function of the Lomax Distribution
Extraction and Subsetting of "sts"
Objects