covid-alberta 0.0.4

This is a small package to look at some of the alberta specific covid data.

Covid Alberta

This is a small package that I have developed to look at some of the alberta specific covid data.

This file will become your README and also the index of your documentation.

Install

pip install covid_alberta

Web Scraper

The albertaC19 is a class that scrapes the updated stats off of the alberta Covid-19 website.

example of using the webscraper

abC19scaper = covid_alberta.albertaC19(outputfolder="")
# I don't plan on writing out the data in this example thus the keywords
ab_totals, ab_regions, ab_testing = abC19scaper.scrape_all(fltypes=None, return_dataframes=True)

Now we can show the dataframes

ab_totals.tail()

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	cum_cases	Confirmed_count	Probable_count	Daily_count	Active_cum	Died_cum	Recovered_cum
2020-04-04	1250	38	19	57	618	23	322
2020-04-05	1308	35	23	58	676	24	382
2020-04-06	1344	20	16	36	712	27	449
2020-04-07	1409	39	26	65	776	27	518
2020-04-08	1423	9	5	14	876	29	518

ab_regions.tail()

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	Calgary_cumulative	Central_cumulative	Edmont_cumulative	North_cumulative	South_cumulative	Unknown_cumulative
2020-04-04	778	61	315	75	19	2
2020-04-05	801	65	340	79	21	2
2020-04-06	821	65	348	86	22	2
2020-04-07	854	72	364	94	23	2
2020-04-08	860	72	368	95	26	2

ab_testing.tail()

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	test_count
2020-04-04	1737
2020-04-05	1112
2020-04-06	1129
2020-04-07	1319
2020-04-08	459

These are all pandas DataFrames. For more info on using pandas check out the pandas cookbook.

analysis

these are functions that I have started working on for some quick analyses of the data. The main one being doubling rates

Doubling times

the calculate_doublingtimes function returns 2 columns.

dtime is how many days our count has been doubling from the first reported case to get to todays case count

dtime_rw is a rolling window calcualtion. So if you window is 6 days it looks at what our doubling rate, starting from the case count 6 days ago, would have to be to get to todays case count.

I started off looking at the rolling window calculation. However the more I look into it the more I'm not happy with using the rolling window. Our information about Covid-19 cases are changing so rapidly, that the rolling window calculation tends to be too noisy and too optimistic to be useful. We can calculate both below and see what they look like

totals_dt = covid_alberta.calculate_doublingtimes(ab_totals, col_suffix="cum_cases", combine_df=False)
regions_dt = covid_alberta.calculate_doublingtimes(ab_regions, col_suffix="cumulative", combine_df=False)
totals_dt.tail()

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	dtime	dtime_rw
2020-04-04	2.818897	7.119992
2020-04-05	2.897670	7.353586
2020-04-06	2.982973	9.613334
2020-04-07	3.059140	11.617191
2020-04-08	3.150442	17.176893

regions_dt.tail()

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	Calgary_dtime	Calgary_dtime_rw	Central_dtime	Central_dtime_rw	Edmont_dtime	Edmont_dtime_rw	North_dtime	North_dtime_rw	South_dtime	South_dtime_rw	Unknown_dtime	Unknown_dtime_rw
2020-04-04	3.019693	7.296903	4.046714	14.735665	3.872364	5.864623	3.692514	8.141493	4.472769	7.609425	0	0
2020-04-05	3.110208	7.587349	4.151191	12.826571	3.956375	6.261873	3.807239	8.008629	4.553405	6.431655	0	0
2020-04-06	3.202070	9.970858	4.317239	18.637702	4.082834	8.636192	3.890285	7.959255	4.709120	6.000000	0	0
2020-04-07	3.286065	12.181763	4.376066	15.441420	4.189037	11.309771	3.966687	8.029614	4.863424	7.289318	0	0
2020-04-08	3.385243	19.656061	4.538143	20.885405	4.323639	15.835158	4.109679	9.387934	4.893159	8.566048	0	0

Plots

Here is some of the plots I've used for looking at the data. For this example I'm using matplotlib. Plotly creates nice plots but is a little harder to include in this documentation since it's hosted on github pages. If you head over to my website I'll post the plotly code and example of the interactive plots there.

import matplotlib.pyplot as plt

# Set defaults and settings
days_to_trim = 1
date_fmt = "%B %d"

# Grab the data we want for the plots and trim the last day off
plt_totals = ab_totals[:-days_to_trim]
plt_total_dt = totals_dt[:-days_to_trim]
plt_regions = ab_regions[:-days_to_trim]
plt_regions_dt = regions_dt[:-days_to_trim]

# use a format dictionary so I only have to set them in one location

fmt = {'alb': {'x_data': plt_totals['cum_cases'],
               'y_data': plt_total_dt['dtime'],
               'last_date': plt_totals.index.strftime(date_fmt)[-1],
               'annot_x': plt_totals['cum_cases'][-1],
               'annot_y': plt_total_dt['dtime'][-1],
               'color': 'green',
               'label': 'Alberta'},
       'cal': {'x_data': plt_regions['Calgary_cumulative'],
               'y_data': plt_regions_dt['Calgary_dtime'],
               'last_date': plt_regions.index.strftime(date_fmt)[-1],
               'annot_x': plt_regions['Calgary_cumulative'][-1],
               'annot_y': plt_regions_dt['Calgary_dtime'][-1],
               'color': 'orange',
               'label': 'Calgary'},
       'edm': {'x_data': plt_regions['Edmont_cumulative'],
               'y_data': plt_regions_dt['Edmont_dtime'],
               'last_date': plt_regions.index.strftime(date_fmt)[-1],
               'annot_x': plt_regions['Edmont_cumulative'][-1],
               'annot_y': plt_regions_dt['Edmont_dtime'][-1],
               'color': 'blue', 
               'label': 'Edmonton'},
      }

# Setup the plot
fig, ax = plt.subplots(figsize=(8,6))

# Create the scatter plots using a loop and the dictionary above
for rgn in ['alb', 'cal', 'edm']:
    ax.plot(fmt[rgn]['x_data'], fmt[rgn]['y_data'], 
            c=fmt[rgn]['color'], label=fmt[rgn]['label'])

# add an annotation to the last point
for rgn in ['alb', 'cal', 'edm']:
    ax.plot(fmt[rgn]['annot_x'], fmt[rgn]['annot_y'], 'o', c=fmt[rgn]['color'])
    ax.text(fmt[rgn]['annot_x'] - 60, fmt[rgn]['annot_y'] + 0.08, fmt[rgn]['last_date'], 
            fontdict={'color': fmt[rgn]['color'], 'size': 8, 'weight': 'bold'})
# fancy up the plot
ax.grid(which='both', linestyle=(0, (5, 3)), lw=0.5)
ax.legend(frameon=True, fancybox=True, shadow=True)
ax.set_ylabel('Doubling Time (Days)', fontdict={'size': 9, 'family': 'sans-serif', 'style':'italic'})
ax.set_xlabel('Cumulative Case Count', fontdict={'size': 9, 'family': 'sans-serif', 'style':'italic'})
title = ax.set_title("Alberta: Doubling Time by Cumulative Cases",
                     fontdict={'fontsize': 10, 'family': 'sans-serif', 'fontweight': 'bold'})