digital disease detection
Recently Published Documents


TOTAL DOCUMENTS

14
(FIVE YEARS 1)

H-INDEX

7
(FIVE YEARS 0)

Author(s):  
Effy Vayena ◽  
Lawrence Madoff

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.


Author(s):  
Jessica S. Schwind ◽  
Stephanie A. Norman ◽  
Dibesh Karmacharya ◽  
David J. Wolking ◽  
Sameer M. Dixit ◽  
...  

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

2016 ◽  
Vol 50 (0) ◽  
Author(s):  
Onicio B Leal-Neto ◽  
George S Dimech ◽  
Marlo Libel ◽  
Wanderson Oliveira ◽  
Juliana Perazzo Ferreira

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.


2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Karina N. Alvarez ◽  
Catherine Ordun ◽  
Jane Blake ◽  
Kirsten A. Simmons ◽  
Keith Hansen ◽  
...  

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.


Sign in / Sign up

Export Citation Format

Share Document