Beginner R methods for syndromic surveillance data validation

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:

January 25, 2018

Leveraging the NSSP R Studio Server to Automate QA Monitoring and Reporting

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 Kentucky’s SyS data, with limited resources. We leveraged the NSSP’s R Studio Server to automate quality assurance (QA) monitoring and reporting to meet these objectives.

Objective:

January 25, 2018

Visualization the dynamic interactive maps for results of spatio-temporal scanning

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.

January 25, 2018

Using R for Disease Surveillance + Enabling Citizen Data Science and Cybersecurity/safety Tips for Scientists

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.

January 24, 2018

Using R Shiny to Share Surveillance Data

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.

November 21, 2017

An introduction to leaflet maps in R

Presented September 19, 2017. 

The main goal of this talk is to demonstrate map making in R using leaflet. We will cover trivial and non-trivial examples. I use the data.table package as my default data container and for all data manipulation. 

September 28, 2017

Data quality monitoring for syndromic surveillance using R: A tidy approach

Presented July 27, 2017.

The inferences we make from data can only be as good as the quality of the data; making sure that we are receiving timely, quality data is important. In this presentation, Mark White will describe a number of functions that he has written to perform data quality checks on Kansas emergency department records from NSSP’s BioSense Platform.

September 21, 2017

Using R Markdown, SQL, and RODBC to Generate Reports

Presented May 31, 2017.

Eric Bakota will go over the results from the survey and then I’ll show a report that we generate at HHD using RMarkdown, SQL, and RODBC. This report uses RODBC to connect to our Electronic Disease Surveillance System (MAVEN) to query data needed for the report. The data are imported to R, where they are processed into the various tables, graphs, charts that are used to generate the report. Automating this report has saved 8-10 hours each month.

September 20, 2017

Machine Learning in R: Detecting Carbon Monoxide Poisoning in Syndromic Surveillance Data

Presented January 26, 2017.

This presentation will describe the steps involved in machine learning and will include a demo an application to detect carbon monoxide poisoning in the Kansas syndromic surveillance data.

September 21, 2017

R Group for Biosurveillance

Mission

The mission of the R Group for Biosurveillance is to connect R users and Advance the Practice of R in Public Health Surveillance

Objectives

December 28, 2018

Pages

Contact Us

NSSP Community of Practice

Email: syndromic@cste.org

 

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