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Review
. 2014 Oct:51:287-98.
doi: 10.1016/j.jbi.2014.04.006. Epub 2014 Apr 16.

Visualization and analytics tools for infectious disease epidemiology: a systematic review

Lauren N Carroll  1 , Alan P Au  2 , Landon Todd Detwiler  3 , Tsung-Chieh Fu  4 , Ian S Painter  5 , Neil F Abernethy  6
Affiliations
Review

Visualization and analytics tools for infectious disease epidemiology: a systematic review

Lauren N Carroll et al. J Biomed Inform. 2014 Oct.

Abstract

Background: A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools.

Methods: We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review.

Results: A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use.

Discussion and conclusion: As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload.

Keywords: Disease surveillance; GIS; Infectious disease; Public health; Social network analysis; Visualization.

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Figures

Figure 1
Figure 1. Increased Reference to Common Complex Data Types
Keyword search for GIS, molecular epidemiology, and social network analysis in PubMed highlights the increase in these terms relative to all PubMed index articles. The frequency of other biomedical informatics terms (usability, electronic health record) is shown for comparison. Although the growth of social network analysis has been more recent, the inset shows that this concept has also experienced rapid growth in the published literature.
Figure 2
Figure 2. Flowchart of Literature Review Process
*Off-topic exclusions: clinical trials, decision-making aids, learning behavior, cognitive behavioral theory, disease or outbreak case studies, (health) information networks, data mining, concept mapping, systems mapping, programming language, ontologies and taxonomies, software methodology or framework, and resource mapping †Full text Exclusions: laboratory methodology, epidemic modeling or statistics, risk mapping, public health interventions, genome mapping, not human infectious disease, architecture or system visualization, software case studies, and healthcare or medical treatment
Figure 3
Figure 3. Common geographic (GIS) visualizations
A dot map (left) uses dots to represent a certain measure or feature displayed over a geographical map. They are often used to present the geographical distribution of various disease cases in infectious disease surveillance. This figure represents hypothetical infectious disease cases in the state of California. Each dot represents a specific disease case. These maps may help identify clusters of disease. In interactive tools, users may click individual cases or select subsets of cases to obtain further information. Individual level data is often aggregated in a choropleth map (middle), which uses graded colors or shades to indicate the values of some aggregate measure in specified areas. This figure shows the incidence rate per 100,000 persons of cases from map (left). Differences in the incidence rates by county are indicated with different shades (green), with a darker color indicating a higher rate. Interactive choropleth maps allow selection of regions to obtain additional information. Individual or aggregate level data may be used to statistically derive a spatial risk gradient (right). Other visualization features may allow zooming/panning of maps, introduction of other map layers such as roads, or selection of color scales.
Figure 4
Figure 4. Dendrogram
A dendrogram, or phylogenetic tree, is a branching diagram or “tree” showing the evolutionary history between biological species or other entities based on their genetic characteristics. Species or entities joined together by nodes represent descendants from a common ancestor and are more similar genetically. This figure shows a hypothetical example of a rooted dendrogram, wherein the horizontal position of individuals represents the genetic distance from a specific progenitor. With the advancement of DNA sequencing technologies, phylogenetic trees have been used widely in infectious disease control to depict the genetic similarities and differences between strains and variants of a certain disease pathogen. Knowing whether infectious diseases occurring in different areas are from the same strain provides key information on the source of infection and how the disease may been transmitted. Interactive features of these visualizations may include the ability to collapse or color/label branches.
Figure 5
Figure 5. Social network diagram
A social network is a graphical representation of social relations or exposures consisting of nodes (individuals within the network) and ties (relationships between individuals). Nodes are usually represented as points or other shapes while ties are represented by lines between the nodes. Differences in the shapes or lines of the diagram may be used to represent different characteristics of the individuals or the relationships. This figure shows a hypothetical example of a force-directed social networks diagram. Social networks analyses in infectious disease control have been gaining importance in the past decade. Examining these social relationships between disease cases and their secondary contacts may be beneficial to tracking the spread of infectious diseases within interconnected social networks. It is especially useful in identifying the index/source case and predicting which individuals are more likely to become infected and further infect others.
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References

    1. Bos L, Blobel B. Semantic interoperability between clinical and public health information systems for improving public health services. Medical and Care Compunetics Four. 2007;4:256. - PubMed
    1. Friede A, Reid JA, Ory HW. CDC WONDER: a comprehensive on-line public health information system of the Centers for Disease Control and Prevention. American Journal of Public Health. 1993;83(9):1289–1294. - PMC - PubMed
    1. Friede A, Blum HL, McDonald M. Public health informatics: how information-age technology can strengthen public health. Annual Review of Public Health. 1995;16(1):239–252. - PubMed
    1. Baker EL, et al. CDC's Information Network for Public Health Officials (INPHO): a framework for integrated public health information and practice. Journal of Public Health Management and Practice. 1995;1(1):43–47. - PubMed
    1. Victor LY, Edberg SC. Global Infectious Diseases and Epidemiology Network (GIDEON): a world wide Web-based program for diagnosis and informatics in infectious diseases. Clinical infectious diseases. 2005;40(1):123–126. - PubMed