Open AccessVisual Analytics for Epidemiologists: Understanding the Interactions Between Age, Time, and Disease with Multi-Panel Graphs
Author(s) -
Kenneth Chui,
Julia Wenger,
Steven A. Cohen,
Ele. Naumova
Publication year - 2011
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0014683
Subject(s) - context (archaeology) , analytics , disease surveillance , public health , data visualization , data science , visual analytics , public health surveillance , population , disease , visualization , computer science , demographics , medicine , artificial intelligence , pathology , demography , environmental health , geography , archaeology , sociology
Background Visual analytics, a technique aiding data analysis and decision making, is a novel tool that allows for a better understanding of the context of complex systems. Public health professionals can greatly benefit from this technique since context is integral in disease monitoring and biosurveillance. We propose a graphical tool that can reveal the distribution of an outcome by time and age simultaneously. Methodology/Principal Findings We introduce and demonstrate multi-panel (MP) graphs applied in four different settings: U.S. national influenza-associated and salmonellosis-associated hospitalizations among the older adult population (≥65 years old), 1991–2004; confirmed salmonellosis cases reported to the Massachusetts Department of Public Health for the general population, 2004–2005; and asthma-associated hospital visits for children aged 0–18 at Milwaukee Children's Hospital of Wisconsin, 1997–2006. We illustrate trends and anomalies that otherwise would be obscured by traditional visualization techniques such as case pyramids and time-series plots. Conclusion/Significance MP graphs can weave together two vital dynamics—temporality and demographics—that play important roles in the distribution and spread of diseases, making these graphs a powerful tool for public health and disease biosurveillance efforts.