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

Streamlining Foodborne Disease Surveillance with Open-Source Data Management Software

The National Surveillance Team in the Enteric Diseases Epidemiology Branch of the Centers for Disease Control and Prevention (CDC) collects electronic data from all state and regional public health laboratories on human infections caused by Campylobacter, Salmonella, Shiga toxin-producing E. coli, and Shigella in LEDS. These data inform annual estimates of the burden of illness, assessments of patterns in bacterial subtypes, and can be used to describe trends in incidence.

January 21, 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

Exploring Chief Complaint Word Co-occurence Visualizations

Presented October 28, 2016.

We are going to briefly explore the tidytext, widyr, and flexdashboard packages to analyze word co-occurrence, look at ngrams, and then visualize the results in word network graphs. Looking at your data in this way can help the user gain an understanding of the underlying data.

September 21, 2017

Epidemics of the “common cold” and the dynamics of severe asthma exacerbation

Common colds are one of the principal causes of severe exacerbations in asthmatic people, reflected in epidemic-like waves of asthma hospitalizations. Most studies do not estimate the effect of infectious causes of exacerbations, and cannot account for how this risk changes through time. 

March 14, 2017

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Email: syndromic@cste.org

 

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