surveillance-package  package:surveillance  R Documentation(latin1)

_O_u_t_b_r_e_a_k _d_e_t_e_c_t_i_o_n _a_l_g_o_r_i_t_h_m_s _f_o_r _s_u_r_v_e_i_l_l_a_n_c_e _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     A package implementing statistical methods for the modeling and
     change-point detection in time series of counts, proportions and
     categorical data. 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.

_D_e_t_a_i_l_s:


       Package:  surveillance
       Type:     Package
       Version:  1.1-0
       Date:     2009-10-14
       License:  GPL version 2 (http://www.gnu.org/licenses/gpl.html)

     'surveillance' is an R package implementing statistical methods
     for the retrospective modeling and prospective change-point
     detection in time series of counts, proportions and categorical
     data. The main application is in the detection of aberrations in
     routine collected public health data seen as univariate and
     multivariate time series of counts, but applications could just as
     well originate from environmetrics, econometrics or social
     sciences. As many methods rely on statistical process control
     methodology, the package is thus also relevant to quality control
     and reliability engineering.

     The fundamental data structure of the package is an S4 class 'sts'
     wrapping observations, monitoring results and date handling for
     multivariate time series. Currently the package contains
     implementations 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 (Hoehle,
     2007), negative binomial CUSUM methods (Hoehle and Mazick, 2009),
     and a detector based on generalized likelihood ratios (Hoehle 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 modelling.  This includes
     e.g. paired binary CUSUM described by Steiner et al. (1999) or
     paired comparison Bradley-Terry modelling described in Hoehle
     (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.

     Furthermore, inference methods for the retrospective infectious
     disease model in Held et al. (2005) and Paul et al. (2008)
     handling multivariate time series of counts. Finally, the fully
     Bayesian approach for univariate time series of counts from Held
     et al. (2006) is also implemented.

_A_u_t_h_o_r(_s):

     Author: M. Hoehle with contributions from T. Correa, M. Hofmann,
     C. Lang, M. Paul, A. Riebler, S. Steiner and V. Wimmer

     Maintainer: Michael Hoehle <hoehle@stat.uni-muenchen.de>

_R_e_f_e_r_e_n_c_e_s:

     surveillance: An R package for the surveillance of infectious
     diseases (2007), M. Hoehle, Computational Statistics, 22(4), pp.
     571-582.

_E_x_a_m_p_l_e_s:

     #Code from an early survey article about the package: Hoehle (2007)
     #available from http://surveillance.r-forge.r-project.org/
     ## Not run: demo(cost)
     #Code from a more recent book chapter about using the package for the
     #monitoring of Danish mortality data (Hoehle, 2009).
     ## Not run: demo(biosurvbook)

