Digital Disease Detection Dashboard: Rapid Detection & Outbreak Management Tool | Zendy

Kari. Alvarez | Zendy; Catherine Ordun | Zendy; Jane Blake | Zendy; Kirsten A. Simmons | Zendy; Keith Hansen | Zendy; Dan Baker | Zendy; Lynda Rowe | Zendy; Yusra Ahmad | Zendy; Donald M. Eby | Zendy; Dimitrios Koutsonanos | Zendy; Steve Escaravage | Zendy; KC Decker | Zendy

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Digital Disease Detection Dashboard: Rapid Detection & Outbreak Management Tool

Author(s) -

Kari. Alvarez,

Catherine Ordun,

Jane Blake,

Kirsten A. Simmons,

Keith Hansen,

Dan Baker,

Lynda Rowe,

Yusra Ahmad,

Donald M. Eby,

Dimitrios Koutsonanos,

Steve Escaravage,

KC Decker

Publication year - 2015

Publication title -

online journal of public health informatics

Language(s) - English

Resource type - Journals

ISSN - 1947-2579

DOI - 10.5210/ojphi.v7i1.5657

Subject(s) - dashboard , computer science , data science , javascript , analytics , data mining , outbreak , world wide web , medicine , pathology

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.

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