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.
R Language
Description
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. Robust digital infrastructure is required to process, validate, and summarize data on approximately 60,000 infections annually while optimizing use of financial and personnel resources.
Objective:
The ledsmanageR, a data management platform built in R, aims to improve the timeliness and accuracy of national foodborne surveillance data submitted to the Laboratory-based Enteric Disease Surveillance (LEDS) system by automating the data processing, validating, and reporting workflow.
Description
To describe an R package that was designed to provide ready implementation of veterinary syndromic surveillance systems, from classified data to the generation of alerts and an html interface.
Introduction
Introduction
The field of veterinary syndromic surveillance (VSS) is developing fast, with countries exploring a great variety of data sources. After implementing two VSS systems we have demonstrated that the steps from classified data to full system implementation can be streamlined, and published a guideline for implementation. All the steps described have been made available in an R package (https:// github.com/nandadorea/vetsyn). We aim to demonstrate the utility and potential of this streamlined approach.
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.
What is R?
R is a language and free software environment for statistical computing and graphics. It compiles and runs on a variety of UNIX platforms, Windows, and MacOS. It is similar to the S language and environment - in fact, much code written in S will run unaltered in R. The R environment is an integrated suite of software facilities for data manipulation, calculation and graphical display. You can learn more about R from the R Project website.
How Can I Get R?
Follow these instructions on how to run ILI R code on your BioSense data locker using R studio.
Description
The use of R is increasing in the public health disease surveillance community. The ISDS pre-conference workshops and newly formed R Group for Surveillance have been well attended and continue to grow in popularity. The use of R in the National Syndromic Surveillance Program (NSSP) has also been of value to many users who wish to analyze and visualize public health data using custom R scripts. This interest in R, combined with a desire from many ESSENCE users to create custom analytics and visualizations, led to a summer internship project to look into the feasibility and ways R could be integrated into ESSENCE.
Objective
The objective of this project is to give users the ability to run custom R scripts from within the ESSENCE system. This capability would allow for custom analytics and visualizations to be baked into the system for daily use. It would also provide a sandbox area for new ideas and features to be tested before being developed more fully into the ESSENCE codebase for a more seamless use in the future. The project must do this while maintaining a secure environment for public health data to reside.
Description
Booz Allen Hamilton is developing a novel bio-surveillance prototype tool, the Digital Disease Detection Dashboard (D4) to address the questions fundamental to daily biosurveillance analysis and decision making: is something unusual happening (e.g., is an outbreak or novel disease emerging)?, What is the probability that what I’m seeing is by chance?, How confident am I that this data is really detecting a signal?, Why is this happening and can I explain it?; and How many cases should I expect? (e.g., magnitude of event over time). These questions focus on detection, confidence, variance, and forecasting and D4 integrates a number of diverse analytical tools and methods that are crucial to a complete biosurveillance program.
Objective
To develop a web-enabled Digital Disease Detection Dashboard (D4) that allows users to statistically model and forecast multiple data streams for public health biosurveillance. D4 is a user-friendly, cloudenabled, and R Shiny-powered application that provides intuitive visualization enabling immediate situational awareness through interactive data displays and multi-factor analysis of traditional and non-traditional data feeds. The objective of D4 is to support public health decision making with high confidence across all four aspects of the biosurveillance continuum—detection, investigation, response, and prevention.