Please use this category to discuss covid-19. Focus particularly on content likely to be of interest to this community - i.e. data driven, technical, practical, accessible. If you’re working on a project, feel free to create a topic and ask for help! Note: you need to read for 10 mins and look at 3 posts before the system lets you create a new topic.
This is a wiki post, so feel free to edit this to add links to data sources, modeling tools, important threads, and so forth:
(Please include details like update frequency, format, scope, fields, etc)
This is a fastai related project, made with fastpages, an open source blogging platform with special features for Jupyter Notebooks. This dashboard has helpful visualizations and links to datasets, and is 100% open source. Furthermore, each dashboard is created with a Jupyter notebook so you can see where the data comes from with all of the code. Pull requests are welcome – showcase your modeling and visualization skills!
- Data is updated every hour by GitHub Actions
- Primary data source is the John Hopkins COVID-19 Data Repository, which is updated several times daily. However there are other datasets
- There has been over 200,000 page views at the time of this writing, so great way to get visibility for your projects.
- A great way to get familiar with fastpages
- Source: University of Toronto School of Public Health
- Updates: Daily
- Format: Google docs spreadsheet (3 tabs)
- Scope: Canada and 12 subregions (Provinces, Territories)
- Fields: See the 2nd tab in the spreadsheet for details.
R Shiny Dashboard
- Includes download handlers to download data powering all graphs as well as information about where the source is for various graphs.
– Filter by state to look at cases, deaths, recovered, hospitalizations data by state. Some available by date, and some graphs have smoothing applied
– Coronavirus by country data including tests, cases, recovered, NPI, mobility, etc. Can filter to change to look at different countries, mobility changes over tie, NPI policy changes (link to codebook to explain what categories are).
–Minimal ability to do some graph transformation in UI such as apply smoothing, log scale, cumulative vs new daily.
(Including tutorials, simulation models, machine learning models, etc)
- Bayesian modeling of growth rate predictions, by Thomas Wiecki one of the authors of PyMC3 - Resources:
- Fill me in!