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ISDS 2012 Conference Abstracts

A Grid Based Approach to Share Public Health
Surveillance Applications - The R Example
Kailah Davis*1 and Julio Facelli1, 2

1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; 2Center of High Performance Computing,
University of Utah, Salt Lake City, UT, USA

Objective
This poster describes an approach which leverages grid technology

for the epidemiological analysis of public health data. Through a vir-
tual environment, users, particularly epidemiologists, and others un-
familiar with the application, can perform on-demand powerful
statistical analyses.

Introduction
Currently, there’s little effective communication and collaboration

among public health departments. The lack of collaboration has re-
sulted in more than 300 separate biosurveillance systems (1), which
are disease specific, not integrated or interoperable, and may be du-
plicative (1). Grid architecture is a promising methodology to aid in
building a decentralized health surveillance infrastructure because it
encourages an ecosystem development culture (2), which has the po-
tential to increase collaboration and decrease duplications.

Methods
This project had two major steps: creation and validation of the

grid service. For the first step [creation of the service], we first de-
termined the parameter set required to execute R from the command
line. We then used the caGrid Introduce toolkit (3) and Grid Rapid
Application Virtualization Interface (gRAVI) (4) to wrap the R com-
mand line interface into a grid service. The service was then deployed
to the caGrid training grid. After deployment, the service was invoked
using the R grid service client which was automatically created by
Introduce and gRAVI. 

Our second step was aimed at validating the service by using using
the grid service client to illustrate the working principles of R in a
grid environment. For this illustration, we selected the article by
Hohle et al (5). In this article, the ‘surveillance’ package was devel-
oped to provide different algorithms for the detection of aberrations
in routinely collected surveillance data. For validation purposes, only
a subset of the analyses presented in the article, namely the Farring-
ton and CUSUM algorithms, were reproduced. Using the grid web
client, we uploaded the necessary data files for processing, as well
as the Rscript which was used to replicate the results of (5). The ap-
plication then ran the R script on the execution machine; this machine
had all the necessary R packages needed for the specific scenario.

Results
The implementation of was validated by showing that the results

of the original paper can be reproduced using gird based version of
R. Figure 1 shows the plots related to the steps described above; the
plots illustrating the Farrington and CUSUM algorithms are seen to
be identical to that in (5).

Conclusions
We demonstrated that it is possible to easily deploy applications for

public health surveillance uses. We conclude that the techniques we
used could be generalized to any application that has a command line

interface. Future work will be aimed creating a workflow to access
data services and grid-enabled text processing and analytic tools. We
believe that by providing a set of examples to demonstrate the bene-
fit of this technology to public health surveillance infrastructure may
provide insight that may lead to a better, more collaborative system
of tools that will become the future of public health surveillance.

Fig 1. Recreated Plots

Keywords
Grid computing; Public health grid; analytical service

Acknowledgments

This work was supported by NLM training grant #T15LM007124 and
CDC Center of Excellence for Public Health Informatics #
1P01HK000069-10.

References

1. Subcommittee NBA. Improving the Nation’s Ability to Detect and Re-
spond to 21st Century Urgent Health Threats: First Report of the Na-
tional Biosurveillance Advisory Subcommittee. 2009.

2. Facelli JC. An agenda for ultra-large-scale system research for global
health informatics. ACM SIGHIT Record. 2012;2(1):12-.

3. Hastings S, Oster S, Langella S, Ervin D, Kurc T, Saltz J. Introduce: an
open source toolkit for rapid development of strongly typed grid serv-
ices. Journal of Grid Computing. 2007;5(4):407-27.

4. Chard K, Tan W, Boverhof J, Madduri R, Foster I, editors. Wrap sci-
entific applications as WSRF grid services using gRAVI. 2009: IEEE.

5.Höhle M, Mazick A. Aberration detection in R illustrated by Danish
mortality monitoring. Biosurveillance: Methods and Case Studies.
2010:215-37.

*Kailah Davis
E-mail: kailah.davis@utah.edu

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e135, 2013