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