campy

A dataset containing the number of human cases of campylobacteriosis and numbers of source samples
positive for Campylobacter for each bacterial subtype.

datasets

Implements a non-parametric source attribution model to attribute
cases of disease to sources in Bayesian framework with source and type effects.
Type effects are clustered using a Dirichlet Process. Multiple times and
locations are supported.

Poppy Miller

sourceR

Fits a Non-Parametric Bayesian Source Attribution Model

Chris Jewell

Jonathan Marshall

Nigel French

campy function

A list containing the human cases (`cases&#39;), source samples (`sources&#39;0 and, prevalences (`prev&#39;).cases: data frame with 91 rows and 2 variables:
<dl class="dl-horizontal">
 <dt>Human</dt><dd>number of human cases of campylobacteriosis between 2005-2008 in the Manawatu
 region of New Zealand</dd>
 <dt>Type</dt><dd>MLST type id for the samples</dd>
</dl>sources: data frame with 690 rows and 3 variables
<dl class="dl-horizontal">
 <dt>Count</dt><dd>number of source samples positive for campylobacteriosis</dd>
 <dt>Source</dt><dd>Source id for the samples</dd>
 <dt>Type</dt><dd>MLST type id for the samples</dd>
</dl>prev: data frame with 6 rows and 4 variables
<dl class="dl-horizontal">
 <dt>Value</dt><dd>Prevalence value (number of positive samples divided by total number of samples)</dd>
 <dt>Source</dt><dd>Source id for the samples</dd>
 <dt>n_positive</dt><dd>number of positive source samples for campylobacter (PCR)</dd>
 <dt>n_total</dt><dd>total number of source samples tested for campylobacter</dd>
</dl>

Format

Human cases of campylobacteriosis and numbers of source samples positive for  — campy

Human cases of campylobacteriosis and numbers of source samples positive for 

campy: Human cases of campylobacteriosis and numbers of source samples positive for

Description

Usage

Arguments

Format