Presented April 25, 2018.
R
Resources related to the programming language R.
Since 2009, the Cook County Department of Public Health (CCDPH) has created and disseminated weekly surveillance reports to share seasonal influenza data with the community and our healthcare partners. Surveillance data is formatted into tables and graphs using Microsoft Excel, pasted into a Word document, and shared via email listserv and our website in PDF format.
Presented February 19, 2018.
Description
Scan statistics is one of the most widely used method for detecting and locating the clusters of disease or health-related events through the space-time dimension. Although the Specific software of SatScan is available for free and easier to use graphical user interface (GUI) interface, the click way and the resulting text format have became obstacles in biosurveillance since automated or reproduction operation and the fast communicate information tool appeared. With the power of R software and rsatscan package, we extended the visualization results to become a faster, more effective communication and motivation tool.
Objective:
The purpose of this article was to provide static and interact mapping for the results' SaTscan with R package thereby reduce the gap between decision-makers and researchers.
Description
In 2016, the CDC funded 12 states, under the Enhanced State Opioid Overdose Surveillance (ESOOS) program, to utilize SyS to increase timeliness of state data on drug overdose events. In order to operationalize the objectives of the grant, there was a need to assess and monitor the quality of Kentuckyâs SyS data, with limited resources. We leveraged the NSSPâs R Studio Server to automate quality assurance (QA) monitoring and reporting to meet these objectives.
Objective:
The aim of this project was to develop a nimble system to both monitor and report on the quality of Kentucky emergency department syndromic surveillance (SyS) data at system-wide and facility levels.
Description
There are currently 123 healthcare facilities sending data to the Washington (WA) State syndromic surveillance program. Of these facilities, 30 are sending to the National Syndromic Surveillance Program's (NSSP) production environment. The remainder are undergoing validation or in queue for validation. Given the large number of WA healthcare facilities awaiting validation, staff within the state syndromic surveillance program developed methods in R to reduce the amount of time required to validate data from an individual facility.
Objective:
To share practical, user-friendly data validation methods in R that result in shorter validation time and simpler code.
The R programming language has become a critical data science tool for the scientific community but has also helped launch a new era of “citizen data scientists” due to the wealth of packages that make it easy to access rich data sources, perform a wide array of computations and produce striking and informative visualizations. This talk will review the history of the ‘cdcfluview’ package, show how it has been used by researchers and citizens, and provide insight into the rationale that created it.
Presented November 21, 2017.
This presentation covers how the shiny package can complement traditional surveillance reporting through online, interactive applications. Kelley demonstrates a shiny application Cook County is currently using to share influenza data and walks through the steps she took to make the application and lessons learned. She reviews portions of the code available on Github here: https://github.com/kb230557/Flu_Shiny_App.
The topics covered in this training include frequency tables, scatter plots, correlation plots, box plots, panels with multiple plots on the same page, formatting/customizing plots, and lattice and ggplot2 packages for elegant visualization.
The topics covered in this training include an introduction to the R statistical package, downloading and installation of R, data management including importing datasets, generating data subsets, adding new variables, how to generate descriptive statistics, and basic box plots, histograms and scatter plots. The training also includes a demonstration of using R with BioSense 2.0 data in a real example of a public health issue.