eRum2020::CovidR
  • About
  • Submit
  • Gallery
    • COVOID: modelling transmission / interventions
    • COVID-19 Data Hub
    • Daily economic indicators
    • Covid_19 Kenya
    • The ten-day coronavirus forecast
    • COVID-19 Bulletin Board Japan
    • 100 k African Hospital and Health Facilities
    • COVID-19 in Belgium: is it over yet?
    • MINT - Monitoring Italian contagion trends
    • The Prime Minister's Speech(es)
    • PangeaCovid19
    • OzCoViz dashboard and the covidrecon package
    • Europe Tracker
    • Fighting misinformation
    • Country-specific Coronavirus Dashboard How-To
    • Covid19-Explorer
    • cRew Symptom Tracker
    • NowCastingCOVID-19
    • Coro2vid-19
    • COVID-19 in India
    • Covid'19 Twitter Analytics
    • Covid-19 in Italy
    • COVID-19 Monitor
    • COVID-19 Canada Data Explorer
    • Covid Shinyline
    • CoronaDash - clustering-analytical tool
    • Coronavirus Tracker
    • Understanding COVID-19 with Excess Deaths
    • COVID-19 Scientific Papers
    • COVID-19 and R blog series
    • Global COVID-19 Dashboard
    • COVID-19 Time-Dependent SIR Model
    • Interactive map of 2019-nCov global distribution
    • Covid19 Data and Perception (CoDaP)
    • Covid19 Shiny App
    • CovidR Example
  • e-Rum2020

coRanavirus early-warning (cRew) Symptom Tracker

coRanavirus early-warning (cRew) Symptom Tracker

by Serdar Korur, Dataatomic [repository]
eRum2020::CovidR

coRanavirus early-warning (cRew) Symptom Tracker

Serdar Korur, Dataatomic

Important Note >> The app uses Google firebase authentication to work. Thus, only authenticated users are able to interact with the app. (More info and a Demo on youTube.)

Covid-19 pandemic caught us off-guard. To avoid this happening again we probably need tools that enable us to react quickly when a new disease emerges and set measures to minimize its spread. In an attempt in this direction, we developed cRew.

coRanavirus early-warning (cRew) tracks Covid-19 / flu like disease symptoms in real-time. The goal is to map in real-time healthy and symptomatic people. As users enter data about their health status, the app monitors temporal and spatial changes and estimates sudden increases or decreases on local risks. The app is built with shinyMobile & firebase, echarts4r and fireData packages.

Vote for "coRanavirus early-warning (cRew) Symptom Tracker"

Like this contribution to see it presented and awarded at the e-Rum2020 CovidR contest pre-conference event

Include the badge for serdar-korur

eRum2020::CovidR

Markdown

[![eRum2020::CovidR](https://badgen.net/https/runkit.io/erum2020-covidr/badge/branches/master/serdar-korur?cache=300)](https://milano-r.github.io/erum2020-covidr-contest/serdar-korur.html)

HTML

<a href="https://milano-r.github.io/erum2020-covidr-contest/serdar-korur.html"><img src="https://badgen.net/https/runkit.io/erum2020-covidr/badge/branches/master/serdar-korur?cache=300" alt="eRum2020::CovidR"/></a>


e-Rum2020 CovidR Contest Created with rmdgallery 0.4.0