- Chui KKH, Wenger JB, Cohen SA, Naumova EN. Visual Analytics for Epidemiologists: Understanding the Interactions Between Age, Time, and Disease with Multi-Panel Graphs. PLoS ONE. 2011;6(2):e14683. In this article, the authors propose the use of a visual analytics tool called multi-panel graphs to facilitate the interaction among age, temporal trends and disease. The authors provide examples of using this technique for influenza, salmonella, and asthma from CMS, Massachusetts DPH and the Children’s hospital of Wisconsin.
- Hafen RP, Anderson DE, Cleveland WS, et al. Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts. BMC Medical Informatics and Decision Making. 2009;9(1):21. Here the authors present a statistical outbreak detection method along with visualizations of data decomposition introducing a method they call STL, for “seasonal-trend decomposition procedure based on loess smoothing.” The authors compared the STL-derived detector to EARS methods C1, C2, and C3 and to a regression detector based on general linear modeling (GLM) for small, medium, and large-scale Emergency Department (ED) time series for detection of artificial lognormal signals at 3 levels. The STL method outperformed the EARS methods for in each situation. It outperformed the GLM method significantly for the smaller signals and slightly for the larger ones.
- Ji X, Chun S, Geller J. Epidemic Outbreak and Spread Detection System Based on Twitter Data. In: He J, Liu X, Krupinski E, Xu G, eds. Health Information Science.Vol 7231. Lecture Notes in Computer Science. Springer Berlin / Heidelberg; 2012:152–163. This study discusses the design and utility of the New Jersey Institute of Technology’s Epidemics Outbreak and Spread Detection System (EOSDS). EOSDS utilizes publicly available information from Twitter to generate three different visualizations of the space and time dimensions of a spreading epidemic: static map, distribution map, and filter map. As an example of the utility of this system they considered a time period during which there was a severe outbreak of listeria in North America and compared the EOSDS visualizations (collecting a test dataset by specifying the keyword “listeria,” and monitoring the period from “09-26-2011” to “09-28-2011”) and CDC official reports – a gold standard. They reported that all three visualizations showed good correlation with CDC reports and concluded that EOSDS may be used as an effective early warning system.
Written by ISDS Program Manager, Tera Reynolds.