Journal Information
Journal ID (publisher-id): OJPHI
ISSN: 1947-2579
Publisher: University of Illinois at Chicago Library
Article Information
©2013 the author(s)
open-access: This is an Open Access article. Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes.
Electronic publication date: Day: 4 Month: 4 Year: 2013
collection publication date: Year: 2013
Volume: 5E-location ID: e9
Publisher Id: ojphi-05-9

Modeling Baseline Shifts in Multivariate Disease Outbreak Detection
Jialan Que*
Fu-Chiang Tsui
University of Pittsburgh, Pittsburgh, PA, USA
*Jialan Que, E-mail: jialan.que@gmail.com

Abstract
Objective

Outbreak detection algorithms monitoring only disease-relevant data streams may be prone to false alarms due to baseline shifts. In this paper, we propose a Multinomial-Generalized-Dirichlet (MGD) model to adjust for baseline shifts.

Introduction

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts.

Methods

Existing multivariate algorithms only model disease-relevant data streams (e.g., anti-fever medication sales or patient visits with constitutional syndrome for detection of flu outbreak). On the contrary, we also incorporate a non-disease-relevant data stream as a control factor.

We assume that the counts from all data streams follow a Multinomial distribution. Given this distribution, the expected value of the distribution parameter is not subject to change during a baseline shift; however, it has to change in order to model an outbreak. Therefore, this distribution inherently adjusts for the baseline shifts. In addition, we use the generalized Dirichlet (GD) distribution to model the parameter, since GD distribution is one of the conjugate prior of Multinomial [2]. We call this model the Multinomial-Generalized-Dirichlet (MGD) model.

Results

We applied MGD model in our previous proposed Rank-Based Spatial Clustering (MRSC) algorithm [3]. We simulated both outbreak cases and baseline shift phenomena. The experiment includes two groups of data sets. The first includes the data sets only injected with outbreak cases, and the second includes the ones with both outbreak cases and baseline shifts. We apply MRSC algorithm and a reference method, the Multivariate Bayesian Scan Statistic (MBSS) algorithm (which only analyzes the disease-relevant data streams) [4], to both data sets. Fig. 2 shows the performance of outbreak detection: the ROC curves and AMOC curves of analyzing the data sets with baseline shifts (solid lines) and without (dashed lines). We can see from Fig. 2 that the performance of MBSS dropped much more significantly than MRSC when analyzing the data sets with baseline shifts.

Conclusions

The MGD model can be a good supplement model used to detect disease outbreaks in order to achieve both better sensitivity and better specificity especially when baseline shifts are present in the data.

ojphi-05-9f1.tif
[Figure ID: f1-ojphi-05-9]
Fig. 1 

Eight data streams of NRDM categories collected by RODS system (Anti-Diarrhea, Anti-Fever Adult, Chest Rubs, Cough/Cold, Baby/Child Electrolytes, Nasal Products, Rash and Thermometers) between Apr. 3, 2011 and Apr. 8, 2011 in Washington DC.


ojphi-05-9f2.tif
[Figure ID: f2-ojphi-05-9]
Fig. 2 

ROC and AMOC curves of MRSC (red) and MBSS (blue). The solid lines represent the two algorithms applied on the data sets injected with both outbreak cases and baseline shift phenomena. The dashed lines represent the two algorithms applied on the data sets injected with outbreak cases only.



Acknowledgments

This research was funded by PA Department of Health syndromic surveillance grant and CDC Center of Excellence grant P01-HK000086.


References
1.. Reis, BY. Kohane, IS. Mandl, KD. An epidemiological network model for disease outbreak detection, PLoS Medicine, 4p. 210 2007
2.. Wong TT. Generalized Dirichlet distribution in Bayesian analysisApplied Mathematics and Computation 97:165–181.1998;
3.. Que, J. Tsui, FC. Rank-based spatial clustering: an algorithm for rapid outbreak detection, J Am Med Inform Assoc, 18218224 2011
4.. Neill, DB. Cooper, GF. A multivariate Bayesian scan statistic for early event detection and characterization, Machine Learning, 29261282 2010

Article Categories:
  • ISDS 2012 Conference Abstracts

Keywords: Biosurveillance, Disease outbreak detection, Algorithm.