surveillance1.24.0 package

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Formatted Time Axis for `"sts"`

Objects

Add Harmonics to an Existing Formula

Aggregate a `disProg`

Object

The Bayes System

Query Transmission to Specified Surveillance Algorithm

The CDC Algorithm

Comparison of Specified Surveillance Systems using Quality Values

CUSUM method

Assign weights to base counts

Fit Poisson GLM of the Farrington procedure for a single time point

Surveillance for Count Time Series Using the Classic Farrington Method

Compute prediction interval for a new observation

Count Data Regression Charts

Hidden Markov Model (HMM) method

Semiparametric surveillance of outbreaks

Computation of Quality Values for a Surveillance System Result

The system used at the RKI

Modified CUSUM method as proposed by Rogerson and Yamada (2004)

Summary Table Generation for Several Disease Chains

Test if Two Model Fits are (Nearly) Equal

Generic animation of spatio-temporal objects

Compute Anscombe Residuals

Calculation of Average Run Length for discrete CUSUM schemes

Non-parametric back-projection of incidence cases to exposure cases us...

Partition of a number into two factors

Bayesian Outbreak Detection Algorithm (BODA)

Bayesian Outbreak Detection in the Presence of Reporting Delays

Calibration Tests for Poisson or Negative Binomial Predictions

CUSUM detector for time-varying categorical time series

Check the residual process of a fitted `twinSIR`

or `twinstim`

Conditional `lapply`

List Coefficients by Model Component

Creating an object of class `disProg`

(DEPRECATED)

Polygonal Approximation of a Disc/Circle

Convert disProg object to sts and vice versa

Surveillance for a count data time series using the EARS C1, C2 or C3 ...

Spatio-Temporal Animation of an Epidemic

Impute Blocks for Extra Stops in `"epidata"`

Objects

Plotting the Evolution of an Epidemic

Summarizing an Epidemic

Continuous-Time SIR Event History of a Fixed Population

Conversion (aggregation) of `"epidataCS"`

to `"epidata"`

or `"sts"`

Spatio-Temporal Animation of a Continuous-Time Continuous-Space Epidem...

Randomly Permute Time Points or Locations of `"epidataCS"`

Plotting the Events of an Epidemic over Time and Space

Update method for `"epidataCS"`

Continuous Space-Time Marked Point Patterns with Grid-Based Covariates

Hook function for in-control mean estimation

Fan Plot of Forecast Distributions

Surveillance for Univariate Count Time Series Using an Improved Farrin...

Determine the k and h values in a standard normal setting

Find decision interval for given in-control ARL and reference value

Find Reference Value

Convert Dates to Character (Including Quarter Strings)

Pretty p-Value Formatting

Fit an Endemic-Only `twinstim`

as a Poisson-`glm`

HCL-based Heat Colors from the `colorspace`

Package

Specify Formulae in a Random Effects HHH Model

Internal Functions Dealing with `hhh4`

Models

Print, Summary and other Standard Methods for `"hhh4"`

Objects

Plots for Fitted `hhh4`

-models

Predictions from a `hhh4`

Model

Plot Simulations from `"hhh4"`

Models

Proper Scoring Rules for Simulations from `hhh4`

Models

Simulate `"hhh4"`

Count Time Series

`update`

a fitted `"hhh4"`

model

Predictive Model Assessment for `hhh4`

Models

Extract Neighbourhood Weights from a Fitted `hhh4`

Model

Power-Law and Nonparametric Neighbourhood Weights for `hhh4`

-Models

Fitting HHH Models with Random Effects and Neighbourhood Structure

Plot Paths of Point Process Intensities

Intersection of a Polygonal and a Circular Domain

Find ISO Week and Year of Date Objects

Knox Test for Space-Time Interaction

Plot the ECDF of a uniform sample with Kolmogorov-Smirnov bounds

Layout Items for `spplot`

Convert Dates of Individual Case Reports into a Time Series of Counts

Run length computation of a CUSUM detector

Compute Suitable k1 x k2 Layout for Plotting

Generate `control`

Settings for an `hhh4`

Model

Import from package `spatstat.geom`

Import from package `spatstat.geom`

Count Number of Instances of Points

Determine Neighbourhood Order Matrix from Binary Adjacency Matrix

Adjust a univariate time series of counts for observed but-not-yet-rep...

Paired binary CUSUM and its run-length computation

Monte Carlo Permutation Test for Paired Individual Scores

Non-Randomized Version of the PIT Histogram (for Count Data)

Verbose and Parallel `lapply`

Plot Observed Counts and Defined Outbreak States of a (Multivariate) T...

Plot a `survRes`

object

Derive Adjacency Structure of `"SpatialPolygons"`

Indicate Polygons at the Border

Prime Number Factorization

Print Quality Value Object

Computes reproduction numbers from fitted models

Import from package `nlme`

Compute indices of reference value using Date class

Extract Cox-Snell-like Residuals of a Fitted Point Process

Sample Points Uniformly on a Disc

Proper Scoring Rules for Poisson or Negative Binomial Predictions

Simulate Point-Source Epidemics

Generation of Background Noise for Simulated Timeseries

Spatio-temporal cluster detection

Diggle et al (1995) K-function test for space-time clustering

Animated Maps and Time Series of Disease Counts or Incidence

Simulate Count Time Series with Outbreaks

Time-Series Plots for `"sts"`

Objects Using `ggplot2`

Create an `sts`

object with a given observation date

Convert an `"sts"`

Object to a Data Frame in Long (Tidy) Format

Class `"sts"`

-- surveillance time series

Aggregate an `"sts"`

Object Over Time or Across Units

Class "stsBP" -- a class inheriting from class `sts`

which allows the ...

Class "stsNC" -- a class inheriting from class `sts`

which allows the ...

Animate a Sequence of Nowcasts

Map of Disease Counts/Incidence accumulated over a Given Period

Animated Map of Disease Incidence (DEPRECATED)

Time-Series Plots for `"sts"`

Objects

Plot Methods for Surveillance Time-Series Objects

Generic Functions to Access `"sts"`

Slots

Subsetting `"sts"`

Objects

Defunct Functions in Package `surveillance`

`surveillance`

: tools:::Rd_package_title("surveillance")

Options of the `surveillance`

Package

`toLatex`

-Method for `"sts"`

Objects

Identify Endemic Components in an Intensity Model

Toy Data for `twinSIR`

Plotting Paths of Infection Intensities for `twinSIR`

Models

Print, Summary and Extraction Methods for `"twinSIR"`

Objects

Profile Likelihood Computation and Confidence Intervals

Simulation of Epidemic Data

Fit an Additive-Multiplicative Intensity Model for SIR Data

Permutation Test for Space-Time Interaction in `"twinstim"`

Temporal and Spatial Interaction Functions for `twinstim`

Plot the Spatial or Temporal Interaction Function of a `twimstim`

Plotting Intensities of Infection over Time or Space

Print, Summary and Extraction Methods for `"twinstim"`

Objects

Plot methods for fitted `twinstim`

's

Profile Likelihood Computation and Confidence Intervals for `twinstim`

...

Simulation from an Isotropic Spatial Kernel via Polar Coordinates

Spatial Interaction Function Objects

Quick Simulation from an Endemic-Only `twinstim`

Simulation of a Self-Exciting Spatio-Temporal Point Process

Stepwise Model Selection by AIC

Temporal Interaction Function Objects

`update`

-method for `"twinstim"`

Fit a Two-Component Spatio-Temporal Point Process Model

Compute the Unary Union of `"SpatialPolygons"`

Randomly Break Ties in Data

Multivariate Surveillance through independent univariate algorithms

Power-Law Weights According to Neighbourhood Order

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

Maintainer: Sebastian Meyer License: GPL-2 Last published: 2024-10-01