Thursday, November 6, 2014, 11:00 AM – 12:30 PM EST (16:00 – 17:30 GMT)
ISDS Research Committee
Linus Schiöler, Statistical Research Unit, Department of Economics, University of Gothenburg, Gothenburg, Sweden Article: Schiöler L. and Frisén, M. (2012): Multivariate outbreak detection,
Journal of Applied Statistics, 39:2, 223-242, http://dx.doi.org/10.1080/02664763.2011.584522
Howard Burkom, Johns Hopkins Applied Physics Laboratory, Laurel, Maryland, USA Article: Burkom H. S., Ramac-Thomas L., Babin S., Holtry R., Mnatsakanyan Z. and Yund C. (2011), An integrated approach for fusion of environmental and human health data for disease surveillance. Statistics in Medicine, 30: 470–479. doi: 10.1002/sim.3976
This presentation is for public health practitioners and methodology developers interested in using statistical methods to combine evidence from multiple data sources for increased sensitivity to disease outbreaks. Methods described will account for practical issues such as delays in outbreak effects between evidence types. Presented examples will include outbreaks from multiple years of authentic data as will as simulations. The ensuing discussions with attendees will explore the role and scope of multivariate surveillance for the situational awareness of public health monitors.
- Overview of methods using multiple data sources for increased sensitivity to disease outbreaks
- Presentation of examples of outbreaks with authentic data
- Discussion of roles and scope of multivariate analysis
Posted Slides – Analytic Methodologies
Analytic Methodologies for Disease Surveillance Using Multiple Sources of Evidence 11-6-14, 11.05 AM from ISDS on Vimeo.
Stat Med. 2011 Feb 28;30(5):470-9. doi: 10.1002/sim.3976. Epub 2011 Feb 3.
An integrated approach for fusion of environmental and human health data for disease surveillance.
Burkom HS1, Ramac-Thomas L , Babin S, Holtry R, Mnatsakanyan Z, Yund C.
This paper describes the problem of public health monitoring for waterborne disease outbreaks using disparate evidence from health surveillance datastreams and environmental sensors. We present a combined monitoring approach along with examples from a recent project at the Johns Hopkins University Applied Physics Laboratory in collaboration with the U.S. Environmental Protection Agency. The project objective was to build a module for the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) to include water quality data with healthindicator data for the early detection of waterborne disease outbreaks. The basic question in the fused surveillance application is ‘What is the likelihood of the public health threat of interest given recent information from available sources of evidence?’ For a scientific perspective, we formulate this question in terms of the estimation of positive predictive value customary in classical epidemiology, and we present a solution framework using Bayesian Networks (BN). An overview of the BN approach presents advantages, disadvantages, and required adaptations needed for a fusedsurveillance capability that is scalable and robust relative to the practical data environment. In the BN project, we built a top-level health/water-qualityfusion BN informed by separate waterborne-disease-related networks for the detection of water contamination and human health effects. Elements of the art of developing networks appropriate to this environment are discussed with examples. Results of applying these networks to a simulated contamination scenario are presented.
Copyright © 2011 John Wiley & Sons, Ltd.