Package: amei
Type: Package
Title: Adaptive Management of Epidemiological Interventions
Version: 1.0-5
Date: 2012-11-01
Author: Daniel Merl <danmerl@gmail.com>, Leah R. Johnson
<lrjohnson@uchicago.edu>, Robert B. Gramacy
<rbgramacy@chicagobooth.edu>, and Marc S. Mangel
<msmangel@ams.ucsc.edu>
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Description: This package provides a flexible statistical framework for
generating optimal epidemiological interventions that are
designed to minimize the total expected cost of an emerging
epidemic while simultaneously propagating uncertainty regarding
underlying disease parameters through to the decision process
via Bayesian posterior inference.  The strategies produced
through this framework are adaptive: vaccination schedules are
iteratively adjusted to reflect the anticipated trajectory of
the epidemic given the current population state and updated
parameter estimates.
License: GPL
LazyLoad: yes
Packaged: 2012-11-02 14:25:50 UTC; bobby
Repository: CRAN
Date/Publication: 2012-11-02 15:11:05

Author: Daniel Merl <danmerl@gmail.com>, Leah R. Johnson <lrjohnson@uchicago.edu>, Robert B. Gramacy <rbgramacy@chicagobooth.edu>, and Marc S. Mangel <msmangel@ams.ucsc.edu>
This package provides a flexible statistical framework for generating optimal
epidemiological interventions that are designed to minimize the total expected
cost of an emerging epidemic while simultaneously propagating uncertainty regarding
underlying disease parameters through to the decision process via Bayesian posterior
inference.  The strategies produced through this framework are adaptive: vaccination
schedules are iteratively adjusted to reflect the anticipated trajectory of the
epidemic given the current population state and updated parameter estimates.

Package: amei
Type: Package
Title: Adaptive Management of Epidemiological Interventions
Version: 1.0-7
Date: 2013-12-13
Author: Daniel Merl <danmerl@gmail.com>, Leah R. Johnson <lrjohnson@uchicago.edu>, Robert B. Gramacy <rbgramacy@chicagobooth.edu>, and Marc S. Mangel <msmangel@ams.ucsc.edu>
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Description:
This package provides a flexible statistical framework for generating optimal
epidemiological interventions that are designed to minimize the total expected
cost of an emerging epidemic while simultaneously propagating uncertainty regarding
underlying disease parameters through to the decision process via Bayesian posterior
inference.  The strategies produced through this framework are adaptive: vaccination
schedules are iteratively adjusted to reflect the anticipated trajectory of the
epidemic given the current population state and updated parameter estimates.
License: GPL
LazyLoad: yes
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-12-13 21:57:38