stsNC-class

reportingTriangle

reportingTriangle,stsNC-method

delayCDF

delayCDF,stsNC-method

score

score,stsNC-method

coerce,sts,stsNC-method

A class inheriting from class <code>sts</code>, but with additional slots
  to store the results of nowcasting.

classes

A package implementing statistical methods for the modeling and
        change-point detection in time series of counts, proportions and
        categorical data, as well as for the modeling of continuous-time
        epidemic phenomena, e.g. discrete-space setups such as the spatially
        enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for
        surveillance data, or continuous-space point process data such as the
        occurrence of disease or earthquakes. Main focus is on outbreak
        detection in count data time series originating from public health
        surveillance of infectious diseases, but applications could just as well
        originate from environmetrics, reliability engineering, econometrics or
        social sciences.
        Currently the package contains implementations of typical
        outbreak detection procedures such as Farrington et al (1996),
        Noufaily et al (2012) or the negative binomial LR-CUSUM method
        described in Hoehle and Paul (2008). Furthermore, inference
        methods for the retrospective infectious disease model in Held
        et al (2005), Held et al (2006), Paul et al (2008) and Paul
        and Held (2011) are provided. A novel CUSUM approach combining
        logistic and multinomial logistic modelling is also included.
        Continuous self-exciting spatio-temporal point processes are
        modeled through additive-multiplicative conditional
        intensities as described in H�hle (2009) ("twinSIR", discrete
        space) and Meyer et al (2012) ("twinstim", continuous space).
        The package contains several real-world data sets, the ability
        to simulate outbreak data, visualize the results of the
        monitoring in temporal, spatial or spatio-temporal fashion.
	Note: The suggested package INLA is unfortunately not available from 
        any mainstream repository - in case one wants to use the 'boda' 
	algorithm one needs to manually install the INLA package 
        as specified at http://www.r-inla.org/download. 

Michael Hhle

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
        Phenomena

stsNC-class function

encoding

The methods are the same as for <code>"<links4class>sts</links4class>"</code>.<p></p><p>  <ul><li><code>signature(from = "sts", to = "stsNC")</code></li></ul>{
      Convert an object of class <code>sts</code> to class <code>stsNC</code>.
    }
    <item>reportingTriangle</item>{<code>signature(x = "stsNC")</code>: extract the
      <code>reportingTriangle</code> slot of an <code>stsNC</code> object.
    }
    <item>delayCDF</item>{<code>signature(x = "stsNC")</code>: extract the
      <code>delayCDF</code> slot of an <code>stsNC</code> object.
    }
    <item>score</item>{<code>signature(x = "stsNC")</code>: extract the
      scoring rules result slot of an <code>stsNC</code> object.
    }</p>

Methods

Class "stsNC" -- a class inheriting from class <code>sts</code> which
  allows the user to store the results of back-projecting 
  surveillance time series

stsNC-class: Class "stsNC" -- a class inheriting from class `sts` which allows the user to store the results of back-projecting surveillance time series

Description

Arguments

encoding

Methods