Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Vignettes

- algo.glrnb: Count data regression charts using the generalized likelihood ratio statistic
- Getting started with outbreak detection
- hhh4: An endemic-epidemic modelling framework for infectious disease counts
- hhh4 (spatio-temporal): Endemic-epidemic modeling of areal count time series
- Monitoring count time series in R: Aberration detection in public health surveillance
- twinSIR: Individual-level epidemic modeling for a fixed population with known distances
- twinstim: An endemic-epidemic modeling framework for spatio-temporal point patterns

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**abattoir:**Abattoir Data**addFormattedXAxis:**Formatted Time Axis for '"sts"' Objects**addSeason2formula:**Add Harmonics to an Existing Formula**aggregate.disProg:**Aggregate a 'disProg' Object**algo.bayes:**The Bayes System**algo.call:**Query Transmission to Specified Surveillance Algorithm**algo.cdc:**The CDC Algorithm**algo.compare:**Comparison of Specified Surveillance Systems using Quality...**algo.cusum:**CUSUM method**algo.farrington:**Surveillance for Count Time Series Using the Classic...**algo.farrington.assign.weights:**Assign weights to base counts**algo.farrington.fitGLM:**Fit Poisson GLM of the Farrington procedure for a single time...**algo.farrington.threshold:**Compute prediction interval for a new observation**algo.glrnb:**Count Data Regression Charts**algo.hmm:**Hidden Markov Model (HMM) method**algo.outbreakP:**Semiparametric surveillance of outbreaks**algo.quality:**Computation of Quality Values for a Surveillance System...**algo.rki:**The system used at the RKI**algo.rogerson:**Modified CUSUM method as proposed by Rogerson and Yamada...**algo.summary:**Summary Table Generation for Several Disease Chains**algo.twins:**Fit a Two-Component Epidemic Model using MCMC**all.equal:**Test if Two Model Fits are (Nearly) Equal**animate:**Generic animation of spatio-temporal objects**anscombe.residuals:**Compute Anscombe Residuals**arlCusum:**Calculation of Average Run Length for discrete CUSUM schemes**backprojNP:**Non-parametric back-projection of incidence cases to exposure...**bestCombination:**Partition of a number into two factors**boda:**Bayesian Outbreak Detection Algorithm (BODA)**bodaDelay:**Bayesian Outbreak Detection in the Presence of Reporting...**calibration:**Calibration Tests for Poisson or Negative Binomial...**campyDE:**Campylobacteriosis and Absolute Humidity in Germany 2002-2011**categoricalCUSUM:**CUSUM detector for time-varying categorical time series**checkResidualProcess:**Check the residual process of a fitted 'twinSIR' or...**clapply:**Conditional 'lapply'**coeflist:**List Coefficients by Model Component**create.disProg:**Creating an object of class 'disProg' (DEPRECATED)**deleval:**Surgical Failures Data**discpoly:**Polygonal Approximation of a Disc/Circle**disProg2sts:**Convert disProg object to sts and vice versa**earsC:**Surveillance for a count data time series using the EARS C1,...**epidata:**Continuous-Time SIR Event History of a Fixed Population**epidata_animate:**Spatio-Temporal Animation of an Epidemic**epidataCS:**Continuous Space-Time Marked Point Patterns with Grid-Based...**epidataCS_aggregate:**Conversion (aggregation) of '"epidataCS"' to '"epidata"' or...**epidataCS_animate:**Spatio-Temporal Animation of a Continuous-Time...**epidataCS_permute:**Randomly Permute Time Points or Locations of '"epidataCS"'**epidataCS_plot:**Plotting the Events of an Epidemic over Time and Space**epidataCS_update:**Update method for '"epidataCS"'**epidata_intersperse:**Impute Blocks for Extra Stops in '"epidata"' Objects**epidata_plot:**Plotting the Evolution of an Epidemic**Browse all...**

View source: R/algo_farrington.R

algo.farrington.assign.weights | R Documentation |

Weights are assigned according to the Anscombe residuals

```
algo.farrington.assign.weights(s, weightsThreshold=1)
```

`s` |
Vector of standardized Anscombe residuals |

`weightsThreshold` |
A scalar indicating when observations are seen as outlier. In the original Farrington proposal the value was 1 (default value), in the improved version this value is suggested to be 2.58. |

Weights according to the residuals

`anscombe.residuals`

algo.glrnb: Count data regression charts using the generalized likelihood ratio statistic
Getting started with outbreak detection
hhh4: An endemic-epidemic modelling framework for infectious disease counts
hhh4 (spatio-temporal): Endemic-epidemic modeling of areal count time series
Monitoring count time series in R: Aberration detection in public health surveillance
twinSIR: Individual-level epidemic modeling for a fixed population with known distances
twinstim: An endemic-epidemic modeling framework for spatio-temporal point patterns

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