Title: sourceR: Classification and Source Attribution of Infectious Agents among Heterogeneous Populations,
Abstract: Zoonotic diseases are a major cause of morbidity, and productivity losses in
both humans and animal populations. Identifying the source of food-borne
zoonoses (e.g. an animal reservoir or food product) is crucial for the
identification and prioritisation of food safety interventions. For many
zoonotic diseases it is difficult to attribute human cases to sources of
infection because there is little epidemiological information on the cases.
However, microbial strain typing allows zoonotic pathogens to be categorised,
and the relative frequencies of the strain types among the sources and in human
cases allows inference on the likely source of each infection. We introduce
sourceR, an R package for quantitative source attribution, aimed at food-borne
diseases. It implements a fully joint Bayesian model using strain-typed
surveillance data from both human cases and source samples, capable of
identifying important sources of infection. The model measures the force of
infection from each source, allowing for varying survivability, pathogenicity
and virulence of pathogen strains, and varying abilities of the sources to act
as vehicles of infection. A Bayesian non-parametric (Dirichlet process)
approach is used to cluster pathogen strain types by epidemiological behaviour,
avoiding model overfitting and allowing detection of strain types associated
with potentially high 'virulence'.
sourceR is demonstrated using Campylobacter jejuni isolate data collected in
New Zealand between 2005 and 2008. It enables straightforward attribution of
cases of zoonotic infection to putative sources of infection by epidemiologists
and public health decision makers. As sourceR develops, we intend it to become
an important and flexible resource for food-borne disease attribution studies.