Using COVID-19 data
with EdelweissData

Overview

  • Case study 1 : Dashboard for Slovenia
  • Case study 2: Estimating cases from deaths
  • What is Edelweiss Data?
  • Using COVID-19 data from ED with Jupyter
  • How to do nightly data imports with Github Actions
  • Using the power of metadata

Dashboad for Slovenia

Dashboad for Slovenia

  • Grassroots effort to collect and communicate data
  • CSV files on Github worked fine initially
  • But country comparision started to strain browser resources

Dashboad for Slovenia

Estimating case numbers from deaths

Estimating case numbers from deaths

  • Limited testing capacity means case numbers are unreliable
  • Deaths are harder to miss
  • Given the IFR (~ 1 in 140), estimate true cases from deaths

10 total deaths

Estimating case numbers from deaths

10 total deaths

1400 infected

23 days earlier

  • Limited testing capacity means case numbers are unreliable
  • Deaths are harder to miss
  • Given the IFR (~ 1 in 140), estimate true cases from deaths

Estimating case numbers from deaths

What is EdelweissData?

A platform for managing tabular datasets

  • with rich metadata support
  • strong versioning
  • interactive web UIs
  • APIs that work with all sorts of tools (Python, R, Excel, KNIME, ...)

4 parts of every dataset

date location cases

Metadata

JSON

📄

README

Schema

date: datetime

location: string

cases: int

How did ED help with the Slovenia Dashboard?

  • Easy importing of data
  • ED provides JSON APIs with powerful filtering
  • Automatic updates every night + full version history

 

How did ED help with the case estimation notebook?

  • Metadata made it possible to easily switch between worldwide and country level datasets
  • Metadata describes columns, enumerates regions, provides links to extended documentation

 

Consume data with your favourite tool

How to unify data sources

location date new_cases total_cases new_deaths total_deaths
state date cases deaths
Bundesland Meldedatum AnzahlFall AnzahlTodesfall
{ "columnNames": {
     "region": "location",
     "date": "date",
     "total-cases": "total_cases",
     "new-cases": "new_cases",
     "total-deaths": "total_deaths",
  	 "new-deaths": "new_deaths"
  }
}
{ "columnNames": {
     "region": "state",
     "date": "date",
     "total-cases": "cases",
     "total-deaths": "deaths",
  }
}
{ "columnNames": {
     "region": "Bundesland",
     "date": "Meldedatum",
     "total-cases": "AnzahlFall",
     "total-deaths": "AnzahlTodesfall",
  }
}

Our world in data Dataset

metadata.json

New York Times Dataset

metadata.json

RKI Dataset

metadata.json

Periodic data imports

One neat way: Github Actions

# This workflow runs as a cron job to download the current version of the New York Times
# covid 19 dataset for the US and publishes a new version of this dataset into edelweiss
# data
name: Update New York Times dataset

on:
  schedule:
  - cron: '15 15 * * *'

jobs:
  test:
    - name: run update
      working-directory: data-import-scripts
      env:
        REFRESH_TOKEN: ${{ secrets.REFRESH_TOKEN }}
      run: python new-york-times.py

Public beta soon ...

Thank you!

These slides:

slides.com/danielbachler/covid19-edelweiss-data