short-paper

Integration and visualization public health dashboard: the medi+board pilot project

Authors:

Patty Kostkova,

Stephan Garbin,

Wendy PanAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 657 - 662

https://doi.org/10.1145/2567948.2579276

Published: 07 April 2014 Publication History

Abstract

Traditional public health surveillance systems would benefit from integration with knowledge created by new situation-aware realtime signals from social media, online searches, mobile/sensor networks and citizens' participatory surveillance systems. However, the challenge of threat validation, cross-verification and information integration for risk assessment has so far been largely untackled. In this paper, we propose a new system, medi+board, monitoring epidemic intelligence sources and traditional case-based surveillance to better automate early warning, cross-validation of signals for outbreak detection and visualization of results on an interactive dashboard. This enables public health professionals to see all essential information at a glance. Modular and configurable to any 'event' defined by public health experts, medi+board scans multiple data sources, detects changing patterns and uses a configurable analysis module for signal detection to identify a threat. These can be validated by an analysis module and correlated with other sources to assess the reliability of the event classified as the reliability coefficient which is a real number between zero and one. Events are reported and visualized on the medi+board dashboard which integrates all information sources and can be navigated by a timescale widget. Simulation with three datasets from the swine flu 2009 pandemic (HPA surveillance, Google news, Twitter) demonstrates the potential of medi+board to automate data processing and visualization to assist public health experts in decision making on control and response measures.

References

[1]

M Salathe, L. Bengtsson, TJ Bodnar, DD Brewer, JS Brownstein, C Buckee, EM Campbell, C Cattuto, S Khandelwal, PL Mabry, A Vespignani. Digital Epidemiology. PLoS Computational Biology 8(7): e1002616. dio:10.1371/journal.pcbi.1002616.

[2]

SI Hay, DB George, CL Moyer, JS Brownstein. Big Data Opportunities for Global Infectious Disease surveillance. PLOS Medicine. April 2013, Volume 10, Issue 4, e1001413.

[3]

P Kostkova. A roadmap to integrated digital public health surveillance: the vision and the challenges. In Proceedings of the 22nd international conference on World Wide Web (WWW '13). 687--694., 2013.

Digital Library

[4]

P Barboza, L Vaillant, A Mawudeku, NP Nelson, DM Hartley, LC Madoff, JP Linge, N Collier, JS Brownstein, R Yangarber, P Astagneu. Evaluation of Epidemic intelligence systems Integrated in the earl Alert in and Reporting Project for the Detection of A/H5N1 Influenza Events. PLOS One, March 2013, Volume 8, Issue 3, e57272.

[5]

http://www.ot.co.uk/global/customers/successstories.htm?sys_action=show&id=661.

[6]

WHO,http://www.who.int/csr/alertresponse/epidemicintelligence/en/index.html.

[7]

Linge JP, Steinberger R, Weber TP, Yangarber R, van der Goot E, Al Khudhairy DH, Stilianakis NI. Internet surveillance systems for early alerting of health threats. EuroSurveill. 2009;14(13):pii=1916. (2009).

[8]

http://healthmap.org/en/?gclid=COmHjfHtiKwCFdQLfAodcH-V-Q.

[9]

M. Keller, M. Blench, H. Tolentino, C.C. Freifeld, K.D, Mandl, A. Mawudeku, G. Eusenbach, J.S. Brownstein. Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance. Emerging Infectious Diseases, Vol. 15, No. 5, May 2009.

[10]

Google Flu Trends, http://www.google.org/flutrends/

[11]

J. Ginsberg, M. H. Mohebbi, R.S. Patel, L. Brammer, M.S. Smolinski, L. Brilliant. Detecting influenza epidemics using search engine query data. Nature Vol 457, 19 February 2009.

[12]

S. Wiseman, P. Kostkova, E. de Quincey, G. Jawaheer. Providing guidance during the swine flu outbreak in 2009: An evaluation study of the National Resource for Infection Control (NRIC). Poster and Abstract in the Proceedings of the 14th International Conference on Infectious Diseases (ICID), March 9--12th 2010, Miami, Florida, US.

[13]

P Kostkova, D Fowler, S Wiseman, JR Weinberg, Major infection events over 5 years: how is media coverage influencing online information needs of health care professionals and the public J Med Internet Res 2013;15(7):e107)

[14]

Lamos, V., de Bie, T., and Cristianini, N. "Flu Detector -- Tracking Epidemics on Twitter". In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), pp. 599--602.

Digital Library

[15]

Lampos, V. and Cristianini, N. "Tracking the flu pandemic by monitoring the Social Web". In Proceedings of the 2nd IAPR Workshop on Cognitive information Processing (CIP2010), pp 411--416, 2010.

[16]

Szomszor, M., Kostkova, P., de Quincey, E. (2010). #swineflu: Twitter Predicts Swine Flu Outbreak in 2009. M Szomszor, P Kostkova (Eds.): ehealth 2010, Springer Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering LNICST 69, pages 18--26, 2011.

[17]

V Lampos, N Cristiani. Nowcasting Events from the Social Web with Statistical Learning. ACM TISM, Vol 3, No 4, Article 72, September 2012.

Digital Library

[18]

A Culotta. Towards detecting influenza epidemics by analyzing Twitter messages. ACM, Proceedings of the SOMA '10, pages 115--122.

Digital Library

[19]

J Gomide, A Veloso, W Meira Jr, V Almeida, F Benevenuto, F Ferraz, M Teixeira. Dengue surveillance based on a computational model of spatio-temporal locality of Twitter, ACM, Web Science 2011, June 14--17, 2011, Germany.

Digital Library

[20]

M Salathe, C.C. Freifeld, S R Mekaru, A.F Tomasulo, J.S. Brownstein. Influenza A (H7N9) and the Importance of Digital Epidemiology. The New England Journal of Medicine. July 3rd 2013, nwjm.org.

[21]

A Signorini, AM Segre, P. M. Polgreen. The Use of Twitter to track levels of Disease Activity and public Health Concern in the U.S. during the Influenza a H1N1 Pandemic. PLOS One. 2011; 6(5): e19467.

[22]

E de Quincey, P Kostkova Early warning and outbreak detection using social networking websites: The potential of twitter, 2nd ehealth conference, Istanbul, Turkey, LNICST Electronic Healthcare, p21--24, 2009.

[23]

M Szomszor, P Kostkova, C St Louis. Twitter informatics: Tracking and understanding public reaction during the 2009 swine flu pandemic. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011, pp. 320--323.

Digital Library

Cited By

Rabiei RBastani PAhmadi HDehghan SAlmasi S(2024)Developing public health surveillance dashboards: a scoping review on the design principlesBMC Public Health10.1186/s12889-024-17841-224:1Online publication date: 6-Feb-2024
https://doi.org/10.1186/s12889-024-17841-2
Chen APark CDarkwa AHolliday RBest M(2024)"We're Not in That Circle of Misinformation": Understanding Community-Based Trusted Messengers Through Cultural Code-SwitchingProceedings of the ACM on Human-Computer Interaction10.1145/36374298:CSCW1(1-36)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637429
Schulze ABrand FGeppert JBöl G(2023)Digital dashboards visualizing public health data: a systematic reviewFrontiers in Public Health10.3389/fpubh.2023.99995811Online publication date: 4-May-2023
https://doi.org/10.3389/fpubh.2023.999958
Show More Cited By

Index Terms

Integration and visualization public health dashboard: the medi+board pilot project
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

Recommendations

A roadmap to integrated digital public health surveillance: the vision and the challenges
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

The exponentially increasing stream of real time big data produced by Web 2.0 Internet and mobile networks created radically new interdisciplinary challenges for public health and computer science. Traditional public health disease surveillance systems ...
Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US
Abstract
Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing risk-standardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. ...
A method for detecting and characterizing outbreaks of infectious disease from clinical reports

Display Omitted We describe a Bayesian method for detecting and characterizing infectious disease outbreaks.The method takes as input clinical reports from an electronic medical record system.The method outputs the probability of an outbreak and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
511
Total Downloads

Downloads (Last 12 months)30
Downloads (Last 6 weeks)4

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rabiei RBastani PAhmadi HDehghan SAlmasi S(2024)Developing public health surveillance dashboards: a scoping review on the design principlesBMC Public Health10.1186/s12889-024-17841-224:1Online publication date: 6-Feb-2024
https://doi.org/10.1186/s12889-024-17841-2
Chen APark CDarkwa AHolliday RBest M(2024)"We're Not in That Circle of Misinformation": Understanding Community-Based Trusted Messengers Through Cultural Code-SwitchingProceedings of the ACM on Human-Computer Interaction10.1145/36374298:CSCW1(1-36)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637429
Schulze ABrand FGeppert JBöl G(2023)Digital dashboards visualizing public health data: a systematic reviewFrontiers in Public Health10.3389/fpubh.2023.99995811Online publication date: 4-May-2023
https://doi.org/10.3389/fpubh.2023.999958
Musah ABrowning EAldosery AValerio Graciano Borges IAmbrizzi TTunali MBaşibüyük SYenigün OMoreno Gde Lima Cda Silva Ados Santos WMassoni TCampos LKostkova P(2023)Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case studyFrontiers in Tropical Diseases10.3389/fitd.2023.10397354Online publication date: 26-May-2023
https://doi.org/10.3389/fitd.2023.1039735
Khodaveisi TDehdarirad HBouraghi HMohammadpour ASajadi FHosseiniravandi M(2023)Characteristics and specifications of dashboards developed for the COVID-19 pandemic: a scoping reviewJournal of Public Health10.1007/s10389-023-01838-z32:4(553-574)Online publication date: 2-Feb-2023
https://doi.org/10.1007/s10389-023-01838-z
Ignatenko ERibeiro MOliveira M(2022)Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory ApproachInternational Journal of Environmental Research and Public Health10.3390/ijerph19171101219:17(11012)Online publication date: 2-Sep-2022
https://doi.org/10.3390/ijerph191711012
Pöhler LKus KTeuteberg F(2021)Understanding Pandemic Dashboard Development: A Multi-level Analysis of Success FactorsInnovation Through Information Systems10.1007/978-3-030-86790-4_22(313-330)Online publication date: 16-Oct-2021
https://doi.org/10.1007/978-3-030-86790-4_22
Chishtie JMarchand JTurcotte LBielska IBabineau JCepoiu-Martin MIrvine MMunce SAbudiab SBjelica MHossain SImran MJeji TJaglal S(2020)Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping ReviewJournal of Medical Internet Research10.2196/1789222:12(e17892)Online publication date: 3-Dec-2020
https://doi.org/10.2196/17892
Gupta AKatarya R(2020)Social media based surveillance systems for healthcare using machine learning: A systematic reviewJournal of Biomedical Informatics10.1016/j.jbi.2020.103500108(103500)Online publication date: Aug-2020
https://doi.org/10.1016/j.jbi.2020.103500
Concannon DHerbst KManley E(2018)Developing a Data Dashboard for Population Health Surveillance: Widening Access to Clinical Trial Findings (Preprint)JMIR Formative Research10.2196/11342Online publication date: 19-Jun-2018
https://doi.org/10.2196/11342
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents