Contains Notebooks analyzing Covid-19 infection data from Johns Hopkins and NY Gimes. So many thanks to the incredible work of Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/ and the NY Times. They provide wonderful geospatial visualization. The notebooks here provide a simple analytical view. They pull data from https://github.com/CSSEGISandData/COVID-19 for wordwide data and for Worldwide data and https://github.com/nytimes/covid-19-data for US data. Both are updated daily, which takes incredible work. With that, people can explore and work on the data from a temporal perspective. Where might we be in two weeks?
Website for this repo: https://deculler.github.io/covid19/
Repo for this website: https://github.com/deculler/covid19
Covid19-Worldwide.ipynb [View Notebook] examines the growth in confirmed cases worldwide at a country level, showing individual countries contribute to the overall picture and examining growth rates per country. Worldwide and per-country projections provide a sense of what the exponential growth is leading to. The data is pulled from https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series.
To run the notebooks live:
US-covid19-nytimes.ipynb [View Notebook] examines the growth in confirmed cases and deaths in the US at the state level, with simple projections.
To run live:
Counties-US-covid19-nytimes.ipynb [View Notebook] examines the growth in confirmed cases and deaths in the US state-by-state at the county level.
To run live:
Cloud infrastuctures for running notebooks
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https://mybinder.org - clones the repo and builds an environment. It takes time, but is quite general.
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Datahub - Is available for members of the UC Berkeley community. It just clones the repo, as the hub environment is all set up. It's fast and retains user state. Instructions for replicating this environment](http://data8.org/zero-to-data-8/deploy/setup_jupyterhub.html)