EDITORIAL: The Next Frontier: Digital Disease Detection in Cyberspace

2014 ◽  
Vol 28 (2) ◽  
pp. 105 ◽  
Author(s):  
Christian Stohler
Keyword(s):  
Disease Detection ◽  
Digital Disease Detection

scholarly journals Digital Disease Detection — Harnessing the Web for Public Health Surveillance

2009 ◽  
Vol 360 (21) ◽  
pp. 2153-2157 ◽  
Author(s):  
John S. Brownstein ◽  
Clark C. Freifeld ◽  
Lawrence C. Madoff
Keyword(s):  
Public Health ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Disease Detection ◽  
Digital Disease Detection ◽  
The Web

scholarly journals Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection

2016 ◽  
Vol 64 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Simon Pollett ◽  
W. John Boscardin ◽  
Eduardo Azziz-Baumgartner ◽  
Yeny O. Tinoco ◽  
Giselle Soto ◽  
...  
Keyword(s):  
Latin America ◽  
Disease Detection ◽  
Digital Disease Detection

scholarly journals Digital Disease Detection Dashboard: Rapid Detection & Outbreak Management Tool

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Karina N. Alvarez ◽  
Catherine Ordun ◽  
Jane Blake ◽  
Kirsten A. Simmons ◽  
Keith Hansen ◽  
...  
Keyword(s):  
Open Source ◽  
Hot Spot ◽  
Health Data ◽  
Management Tool ◽  
Weather Data ◽  
Disease Detection ◽  
Outbreak Management ◽  
Public Health Data ◽  
R Shiny ◽  
Digital Disease Detection

The Digital Disease Detection Dashboard (D4) provides an analytics environment to conduct hypothesis testing, hot spot geolocations, and forecasting in a centralized dashboard. Methods such as linear regression, LOESS, and SIR modeling are implemented R, an open-source programming language. Visualizations utilize Javascript libraries and are rendered using R-Shiny. Currently, D4 contains 15 epidemiological datasets from the CDC including foodborne illness cases, influenza patient counts and positive lab confirmations, and unconventional public health data like weather data. D4’s objective is to use powerful statistical models and rigorous visualizations to analyze multivariable associations to specific outcomes using open source code.

scholarly journals Digital disease detection and participatory surveillance: overview and perspectives for Brazil

2016 ◽  
Vol 50 (0) ◽  
Author(s):  
Onicio B Leal-Neto ◽  
George S Dimech ◽  
Marlo Libel ◽  
Wanderson Oliveira ◽  
Juliana Perazzo Ferreira
Keyword(s):  
Data Mining ◽  
Health Services ◽  
Mobile Applications ◽  
Digital Age ◽  
Scientific Field ◽  
Disease Detection ◽  
Data Source ◽  
Digital Disease Detection ◽  
Participatory Surveillance ◽  
The Web

ABSTRACT This study aimed to describe the digital disease detection and participatory surveillance in different countries. The systems or platforms consolidated in the scientific field were analyzed by describing the strategy, type of data source, main objectives, and manner of interaction with users. Eleven systems or platforms, developed from 1996 to 2016, were analyzed. There was a higher frequency of data mining on the web and active crowdsourcing as well as a trend in the use of mobile applications. It is important to provoke debate in the academia and health services for the evolution of methods and insights into participatory surveillance in the digital age.

scholarly journals Online surveillance of media health event reporting in Nepal: digital disease detection from a One Health perspective

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Jessica S. Schwind ◽  
Stephanie A. Norman ◽  
Dibesh Karmacharya ◽  
David J. Wolking ◽  
Sameer M. Dixit ◽  
...  
Keyword(s):  
One Health ◽  
Disease Detection ◽  
Event Reporting ◽  
Health Event ◽  
Digital Disease Detection

What Can Digital Disease Detection Learn from (an External Revision to) Google Flu Trends?

2014 ◽  
Vol 47 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Mauricio Santillana ◽  
D. Wendong Zhang ◽  
Benjamin M. Althouse ◽  
John W. Ayers
Keyword(s):  
Disease Detection ◽  
Digital Disease Detection
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