An interface for visualizing and analysing the see19 dataset
Project description
see19
An aggregation dataset and interface for visualizing and analyzing Coronavirus Disease 2019 aka COVID19 aka C19
Dataset Last Updated June 12, 2020
May 31, 2020 Update
Upgrade to version 0.3 is complete. Please exercise caution if switching to this version as there have been a number of significant changes / additions that might impact your prior work.
SUMMARY OF UPDATES
1. Testset Graduation
- Test counts and Apple mobility data have been moved into the main dataset.
- Reporting on testing continues to be inconsistent around the world. Many countries have only just begun reporting and many report on an infrequent basis (weekly or worse). Where there are gaps in daily figures, non-linear interpolation is used to smooth figures. Several key regions including Brazil and France have very minimal data at all.
2. Added filter functionality
When instanting a CaseStudy
instance:
- You can now pass any of
region_id
,region_code
, orregion_name
toregions
/exclude_regions
in a single iterable.region_code
column has been added, and is either simply a replica ofcountry_code
or the accepted abbreviation of the province or state. i.e. Alberta'sregion_code
is AB. country_code
andcountry_id
now also acceptable incountries
/exclude_countries
- pandas Series and numpy arrays are now acceptable iterables for these filters as well.
3. Miscellaneous
- To access the testset via
get_baseframe
, settest=True
- Added progress bar for
get_baseframe()
(a couple hours I won't ever get back) - Additional styling attributes to most chart make() functions
- Added exception to catch when a
country_w_sub
is provided as region whencountry_level=False
- when
USA
is filter viacountries
, see19 now automatically excludes the country of Georgia. This was a major personal irritant of mine, but if you have the need you can simply include Georgia incountries
as well.
Latest Analysis
How Effective Is Social Distancing?
What Factors Are Correlated With COVID19 Fatality Rates?
The Dataset
The dataset is in csv
format and can be found here
You can find relevant statistics and detailed sourcing in the Guide
The Package
the see19
package is available on pypi and can be installed as follows:
pip install see19
The package provides a helpful pandas
-based interface for working with the dataset. It also provides several visualization tools
The Guide
The Guide details data sources, structure, functionality, and visualization tools.
Purpose
"It is better to be vaguely right than exactly wrong."
- Carveth Read, Logic, Chapter 22
see19 is an early stage attempt to aggregate various data sources and analyze their impact (together and in isolation) on the virulence of SARS-CoV2.
- Ease-of-use is paramount, thus, all data from all sources have been compiled into a single structure, readily consumed and manipulated in the ubiquitous
csv
format.
see19 aggregates the following data:
- COVID19 Data Characteristics:
- Cumulative Case, Fatality, and Testing statistics for each region on each date
- State / Provincial-level data available for
- Factor Data Characteristics available for most regions include:
- Longitude / Latitude, Population, Demographic Segmentation, Density
- Climate Characteristics including temperatue and uvb radiation
- Historical Health Outcomes
- Travel Popularity
- Social Distancing Implementation
- And more and counting ...
There is no single all-encompassing data from an undoubted source that will serve the needs of every user for every use case. Thus, the dataset as it stands is an ad-hoc aggregation from multiple sources with eyeball-style approximations used in some instances. But while the dataset's imperfections are numerous, they cannot blunt the power of the insights that can be gleaned from an early exploratory analysis.
In addition to the dataset, see19
is a python package that provides:
- Helpful
pandas
-based interface for manipulating the data - Visualization tools in
bokeh
andmatplotlib
to compare factors across multiple dimensions .. - Statistical analysis is also a goal of the project and I expect to add such analysis tools as time progresses. Until then, the data is available for all.
Suggestions For Additional Data
I am always on the hunt for new additions to the dataset. If you have any suggestions, please contact me. Specifically, if you are aware of any datasets that might integrate nicely with see19
in the following realms:
- German daily, state-level counts
- Russian daily, state-level counts
- India daily, sate-level counts
- State or city level travel data
- Global Commercial Airline route data (there seems to be plenty available, except only for a whopping price)
Quick Demo
You can very quickly use see19 to develop visuals for COVID19 analysis and presentation.
The see19
package can be installed via pip
.
pip install see19
Then simply:
# Required to use Bokeh with Jupyter notebooks
from bokeh.io import output_notebook, show
output_notebook()
from see19 import get_baseframe, CaseStudy
baseframe = get_baseframe()
regions = ['Germany', 'Spain']
casestudy = CaseStudy(baseframe, regions=regions, count_categories='deaths_new_dma_per_1M')
label_offsets = {'Germany': {'x_offset': 8, 'y_offset': 8}, 'Spain': {'x_offset': 5, 'y_offset': 5}}
p = casestudy.comp_chart.make(comp_type='multiline', label_offsets=label_offsets, width=750)
show(p)
%matplotlib inline
regions = list(baseframe[baseframe['country'] == 'Brazil'] \
.sort_values(by='population', ascending=False) \
.region_name.unique())[:20]
casestudy = CaseStudy(
baseframe, count_dma=5,
factors=['temp'],
regions=regions, start_hurdle=10, start_factor='cases', lognat=True,
)
kwargs = {
'color_factor': 'temp',
'fs_xticks': 16, 'fs_yticks': 12, 'fs_zticks': 12,
'fs_xlabel': 12, 'fs_ylabel': 18, 'fs_zlabel': 18,
'title': 'Daily Deaths in Brazil as of May 2',
'x_title': 0.499, 'y_title': 0.738, 'fs_title': 22, 'rot_title': -9.5,
'x_colorbar': 0.09, 'y_colorbar': .225, 'h_colorbar': 20, 'w_colorbar': .01,
'a_colorbar': 'vertical', 'cb_labelpad': -57,
'tight': True, 'abbreviate': 'first', 'comp_size': 10,
}
p = casestudy.comp_chart4d.make(comp_category='deaths_new_dma_per_1M', **kwargs)
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