Digital Disease Detection Dashboard: Rapid Detection & Outbreak Management Tool

Authors

  • Karina N. 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

DOI:

https://doi.org/10.5210/ojphi.v7i1.5657

Abstract

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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Published

2015-02-26

How to Cite

Alvarez, K. N., Ordun, C., Blake, J., Simmons, K. A., Hansen, K., Baker, D., Rowe, L., Ahmad, Y., Eby, D. M., Koutsonanos, D., Escaravage, S., & Decker, K. (2015). Digital Disease Detection Dashboard: Rapid Detection & Outbreak Management Tool. Online Journal of Public Health Informatics, 7(1). https://doi.org/10.5210/ojphi.v7i1.5657

Issue

Section

Panel Presentations