YJBIN 2161 No. of Pages 12, Model 5G 18 April 2014 Journal of Biomedical Informatics xxx (2014) xxx–xxx 1 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin 2 Methodological Review 6 4 Visualization and analytics tools for infectious disease epidemiology: 7 5 A systematic review a a b c d 8 Q1 Lauren N. Carroll , Alan P. Au , Landon Todd Detwiler , Tsung-chieh Fu , Ian S. Painter , a,d,⇑ 9 Q2 Neil F. Abernethy 10 Q3 a Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican St., Box 358047, Seattle, WA 98109, United States 11 b Department of Biological Structure, University of Washington, 1959 NE Pacific St., Box 357420, United States 12 c Department of Epidemiology, University of Washington, 850 Republican St., Box 358047, Seattle, WA 98109, United States 13 d Department of Health Services, University of Washington, 1959 NE Pacific St., Box 359442, Seattle, WA 98195, United States 1415 1617 article info abstract 3219 20 Article history: Background: A myriad of new tools and algorithms have been developed to help public health professionals 33 21 Received 13 September 2013 analyze and visualize the complex data used in infectious disease control. To better understand approaches 34 22 Accepted 3 April 2014 to meet these users’ information needs, we conducted a systematic literature review focused on the land- 35 23 Available online xxxx scape of infectious disease visualization tools for public health professionals, with a special emphasis on 36 geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives 37 24 Keywords: of this review are to: (1) identify public health user needs and preferences for infectious disease informa- 38 25 Visualization tion visualization tools; (2) identify existing infectious disease information visualization tools and charac- 39 26 Infectious disease terize their architecture and features; (3) identify commonalities among approaches applied to different 40 27 Public health 28 Disease surveillance data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools. 41 29 GIS Methods: We identified articles published in English from January 1, 1980 to June 30, 2013 from five bib- 42 30 Social network analysis liographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public 43 31 health users, or usability of information visualizations were included in the review. 44 Results: A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences 45 and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The 46 architecture of the tools was inconsistently described, and few tools in the review discussed the incorpo- 47 ration of usability studies or plans for dissemination. Many studies identified concerns regarding data shar- 48 ing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to 49 explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited 50 barriers to widespread adoption included lack of organizational support, access issues, and misconceptions 51 about tool use. 52 Discussion and conclusion: As the volume and complexity of infectious disease data increases, public health 53 professionals must synthesize highly disparate data to facilitate communication with the public and inform 54 decisions regarding measures to protect the public’s health. Our review identified several themes: consid- 55 eration of users’ needs, preferences, and computer literacy; integration of tools into routine workflow; 56 complications associated with understanding and use of visualizations; and the role of user trust and 57 organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, 58 highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infec- 59 tious disease control and prevention. Future work should address methods for representing uncertainty 60 and missing data to avoid misleading users as well as strategies to minimize cognitive overload. 61 Other: Funding for this study was provided by the NIH (Grant# 1R01LM011180-01A1). 62 Ó 2014 Published by Elsevier Inc. 6563 6664 67 68 Abbreviations: GIS, geographic information systems; PHIN, Public Health 1. Introduction Information Network; SVG, scalable vector graphics; DHTML, dynamic HTML. Q4 ⇑ Corresponding author at: Department of Biomedical Informatics and Medical In the last 20 years, an increasing focus on the need for infor- 69 Education, University of Washington, 850 Republican St., Box 358047, Seattle, WA matics and analytics in public health has resulted in a growing 70 98109, United States. investment in information systems [1–7]. This investment has gen- 71 E-mail addresses: [email protected] (L.N. Carroll), [email protected] (A.P. Au), det@uw. 72 edu (L.T. Detwiler), [email protected] (T.-c. Fu), [email protected] (I.S. Painter), neila@uw. erated a myriad of new tools for different public health activities edu (N.F. Abernethy). and jurisdictions, including tools and systems developed by 73 http://dx.doi.org/10.1016/j.jbi.2014.04.006 1532-0464/Ó 2014 Published by Elsevier Inc. Please cite this article in press as: Carroll LN et al. Visualization and analytics tools for infectious disease epidemiology: A systematic review. J Biomed Inform (2014), http://dx.doi.org/10.1016/j.jbi.2014.04.006 YJBIN 2161 No. of Pages 12, Model 5G 18 April 2014 2 L.N. Carroll et al. / Journal of Biomedical Informatics xxx (2014) xxx–xxx 74 federal, state and local governments, as well as research organiza- 140 75 tions [8–12]. Advances in electronic reporting and interoperability, 10 120 8 76 computer technology, biotechnology (e.g. genetic sequencing), and 6 77 other methods (e.g. social network analysis and geographic infor- 100 4 78 mation systems) have put pressure on the informatics discipline 80 2 79 and public health practitioners alike to translate these advances 0 80 into common practice [1,7,13,14]. This pressure has been particu- 60 81 larly acute for the surveillance and management of infectious dis- 2000 2002 2004 2006 2008 2010 2012 40 82 eases with pandemic or bioterrorism potential [7,15–17]. 83 To characterize the variety of tools and analytical approaches 20 84 developed for infectious disease control, we conducted a system- Per 100,000 Articles in MEDLINE 0 85 atic literature review of informatics tools for infectious diseases, 86 with a focus on platforms for information visualization. In this 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 87 review, we assessed the current landscape of these tools in terms Publication Year GIS social network analysis 88 of information needs and user preferences, features and system molecular epidemiology usability 89 architectures of existing tools, as well as usability and adoption electronic health/medical record 90 considerations. Due to the challenges of integrating, analyzing, Fig. 1. Increased reference to common complex data types. Keyword search for GIS, 91 and displaying public health data, particularly new types of data molecular epidemiology, and social network analysis in PubMed highlights the 92 encountered in public health, this review places a special emphasis increase in these terms relative to all PubMed index articles. The frequency of other 93 on efforts to visualize geographic information systems (GIS), biomedical informatics terms (usability, electronic health record) is shown for 94 molecular epidemiology, and social networks. 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. 95 Q5 1.1. Background 96 Since John Snow first plotted cholera cases on a map of London, managing redundancies as well as incomplete data [1,17,29,43– 138 97 graphs and visualizations have played important roles in epidemi- 46]. For example, public health practitioners and researchers are 139 98 ology, supporting communication, aggregation, analysis, and use of faced with integrating diverse data sources such as mortality data 140 99 data for hypothesis testing and decision making [18,19]. In the (e.g. autopsy reports), clinical data (e.g. laboratory reports, immu- 141 100 electronic age, computer-aided generation of charts, maps, and nization records), geographical data (e.g. address of work, resi- 142 101 reports have enabled a further increase in the use of visualization dence, preschool), relationships (e.g. names of family, friends, 143 102 tools to supplement individual-level clinical data and population- partners), patient and pathogen genetics, medical imaging, travel 144 103 level statistics [7,15]. Infectious disease burden in the population, plans, and timelines. Each of these types of information can be 145 104 whether measured for programmatic or outbreak management recorded, stored, accessed, evaluated, and displayed in many dif- 146 105 purposes, is now commonly analyzed in terms of geographic ferent systems and formats. Organizations are therefore challenged 147 106 distribution, clinical risk factors, demographics, molecular and to maximize the potential of this flood of data to impact public 148 107 phylogenetic features, or sources