R Shiny

The CDC provides data on incidences of diseases on its website (https://data.cdc.gov/). Data is available at national, regional, and state levels, and is uploaded to the CDC’s website on a weekly basis. The CDCPlot web application (available at https://michaud.shinyapps.io/ CDCPlot/), built using the Shiny package in R, provides a quick and user-friendly method of visualizing this data. Users are able to the select timeframes, locations, and diseases which they wish to view, and plots are produced. There is an optional alert threshold, which will alert users when a disease increases significantly from one week to the next. In addition, CDCPlot provides visualizations of CDC data on Pneumonia and Influenza mortality.

Objective

To demonstrate the current features and functionality of the CDCPlot application, and to introduce potential new features of the application. 

The French syndromic surveillance system SursaUD® has been set up by Santé publique France, the national public health agency (formerly French institute for public health - InVS) in 2004. In 2016, the system is based on three main data sources: the attendances in about 650 emergency departments (ED), the consultations to

 62 emergency general practitioners’ (GPs) associations SOS Médecins and the mortality data from 3,000 civil status offices [1]. Daily, about 60,000 attendances in ED (88% of the national attendances), 8,000 visits in SOS Médecins associations (95% of the national visits) and 1,200 deaths (80% of the national mortality) are recorded all over the territory and transmitted to Santé publique France. About 100 syndromic groupings of interest are constructed from the reported diagnostic codes, and monitored daily or weekly, for different age groups and geographical scales, to characterize trends, detect expected or unexpected events (outbreaks) and assess potential impact of both environmental and infectious events. All-causes mortality is also monitored in similar objectives. Two user-friendly interactive web applications have been developed using the R shiny package [2] to provide a homogeneous framework for all the epidemiologists involved in the syndromic surveillance at the national and the regional levels.

Objective

The presentation describes the design and the main functionalities of two user-friendly applications developed using R-shiny to support the statistical analysis of morbidity and mortality data from the French syndromic surveillance system SurSaUD.

State HIV offices routinely produce fact sheets, epidemiologic profiles, and other reports from the eHARS (Enhanced HIV/AIDS Reporting System) database which was created and is maintained by the CDC. The eHARS software is used throughout the United States to monitor the HIV epidemic and evaluate HIV prevention programs and policies. Due to limited variability of eHARS throughout the United States, software developed to analyze and visualize data using the eHARS database schema may be useful to many state HIV offices. Software developed based on the eHARS database schema could reduce the time required for analysis and production of reports.

The R software environment for statistical computing is an open source project with a thriving community of users who continue to expand R’s analysis capacity through the addition of packages. A package is “a standardized collection of material extending R, e.g. providing code, data, or documentation”. Shiny is one example of a user-developed package which easily allows R users to create interactive web applications from analytical software.

Objective

Describe the development process and function of a data dashboard for state HIV surveillance and discuss the benefits of creating interactive data dashboards in the R software environment.

The Biosurveillance Ecosystem (BSVE) is a biological and chemical threat surveillance system sponsored by the Defense Threat Reduction Agency (DTRA). BSVE is intended to be user-friendly, multi-agency, cooperative, modular and threat agnostic platform for biosurveillance [2]. In BSVE, a web-based workbench presents the analyst with applications (apps) developed by various DTRAfunded researchers, which are deployed on-demand in the cloud (e.g., Amazon Web Services). These apps aim to address emerging needs and refine capabilities to enable early warning of chemical and biological threats for multiple users across local, state, and federal agencies. Soda Pop is an app developed by Pacific Northwest National Laboratory (PNNL) to meet the current needs of the BSVE for early warning and detection of disease outbreaks. Aimed for use by a diverse set of analysts, the application is agnostic to data source and spatial scale enabling it to be generalizable across many diseases and locations. To achieve this, we placed a particular emphasis on clustering and alerting of disease signals within Soda Pop without strong prior assumptions on the nature of observed diseased counts.

Objective

To introduce Soda Pop, an R/Shiny application designed to be a disease agnostic time-series clustering, alarming, and forecasting tool to assist in disease surveillance “triage, analysis and reporting” workflows within the Biosurveillance Ecosystem (BSVE). In this poster, we highlight the new capabilities that are brought to the BSVE by Soda Pop with an emphasis on the impact of metholodogical decisions.

Data sets from disparate sources widely vary in the number and type of factors which most hamper integrity and timeliness of the data. To maintain high quality data, data sets must be regularly assessed, particularly for those vulnerabilities that each is especially prone to due to the methods involved in collecting the data. For surveillance practitioners charged with monitoring data from multiple data sources, keeping track of the issues that each data set is susceptible to, and quickly identifying any inconsistencies or deviations from normal trends, may be a challenge. An application that can track all those issues, and trigger alerts when patterns diverge from what is expected, could help to enhance the efficiency and effectiveness of the surveillance efforts.

Objective

An interactive, point-and-click application was developed to facilitate the routine assessment of known data quality factors that compromise the integrity and timeliness of data sets used at the Marion County Public Health Department (MCPHD). The code (and associated documentation) for this application is being made available for other surveillance practitioners to adopt.

A variety of government reports have cited challenges in coordinating national biosurveillance efforts at strategic and tactical levels. The General Accountability Office (GAO), an independent nonpartisan agency that investigates how the federal government funding and performs analysis at the request of congressional committees or by public mandate, has published 64 reports on biosurveillance since 2005. The aim of this project is to better characterize these issues by collecting and analyzing a sample of publicly documented biosurveillance systems, and making our data and results available for the public health community to review and evaluate. This study openly publishes the data files of information collected (i.e. CSV, XLS), the Python NLP scripts, and a freely available web-based application developed in R Shiny that filters against the 227 biosurveillance systems and activities to promote a more transparent understanding of how public health practitioners conduct surveillance activities.

Objective

The objective of this project is to advance the science of biosurveillance by providing a user curated cataloging system, to be used across health department and other users, that advances daily surveillance operations by better characterizing three key issues in available surveillance systems: duplication in biosurveillance activities; differing perspectives and analyses of the same data; and inadequate information sharing.