title: Visual analytics for epidemiologists: understanding the interactions between age, time, and disease with multi-panel graphs.
abstract: 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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, Massachusetts, United States of America. kenneth.chui@tufts.edu
authors.forename: Kenneth K H
authors.initials: KK
authors.lastname: Chui
authors.forename: Julia B
authors.initials: JB
authors.lastname: Wenger
authors.forename: Steven A
authors.initials: SA
authors.lastname: Cohen
authors.forename: Elena N
authors.initials: EN
authors.lastname: Naumova
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001249:Asthma / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D003657:Decision Making; D004194:Disease; D004812:Epidemiologic Methods; D005260:Female; D005504:Food Analysis / Q000331:legislation & jurisprudence; D059022:Food Safety; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007251:Influenza, Human / Q000453:epidemiology; D008297:Male; D012475:Salmonella / Q000302:isolation & purification; D012480:Salmonella Infections / Q000453:epidemiology / Q000382:microbiology; D012621:Seasons; D013997:Time Factors; D055815:Young Adult