The special function <code>cox</code> marks terms in formulae of the functions
<code><a href="/link/twinSIR?package=surveillance&version=1.5-4" rd-options="" data-mini-rdoc="surveillance::twinSIR">twinSIR</a></code> and <code><a href="/link/simEpidata?package=surveillance&version=1.5-4" rd-options="" data-mini-rdoc="surveillance::simEpidata">simEpidata</a></code> as endemic components,
i.e. variables acting multiplicatively on the baseline infection intensity.<p></p><p>Technically, this function is implemented as <code>function(x) {x}</code> and
defined as <dquote>special</dquote> in <code><a href="/link/terms.formula?package=surveillance&version=1.5-4" rd-options="" data-mini-rdoc="surveillance::terms.formula">terms.formula</a></code>.</p>

internal

twinSIR_cox

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 Stroup et. al (1989),
        Farrington et al (1996), Rossi et al (1999), Rogerson and
        Yamada (2001), a Bayesian approach, negative binomial CUSUM
        methods and a detector based on generalized likelihood ratios.
        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
        datasets, the ability to simulate outbreak data, visualize the
        results of the monitoring in temporal, spatial or
        spatio-temporal fashion.

twinSIR_cox: Identify Endemic Components in an Intensity Model

Description

Arguments

See Also

Examples