Germany: SK Hof (Bayern)¶

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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
Notebook executed on: 07/03/2023 11:58:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Hof", weeks=5);
2023-03-07T11:58:18.462344 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 90 90 100 100 110 110 120 120 7-day incidence rate (per 100K people) 84.2 SK Hof, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Hof cases daily growth factor Germany-SK Hof cases daily growth factor (rolling mean) Germany-SK Hof estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Hof deaths daily growth factor Germany-SK Hof deaths daily growth factor (rolling mean) Germany-SK Hof estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-SK Hof doubling time cases (rolling mean) 0.00 4.51 9.02 13.54 daily change Germany-SK Hof new cases (rolling 7d mean) Germany-SK Hof new cases 0.000 0.226 0.451 0.677 0.902 daily change Germany-SK Hof new deaths (rolling 7d mean) Germany-SK Hof new deaths 0.000 0.209 0.418 0.627 0.836
In [4]:
overview(country="Germany", subregion="SK Hof");
2023-03-07T11:58:40.556262 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 84.2 SK Hof, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 600 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5 10 15 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Hof cases daily growth factor Germany-SK Hof cases daily growth factor (rolling mean) Germany-SK Hof estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Hof deaths daily growth factor Germany-SK Hof deaths daily growth factor (rolling mean) Germany-SK Hof estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 6000 8000 cases doubling time [days] Germany-SK Hof doubling time cases (rolling mean) Germany-SK Hof doubling time deaths (rolling mean) 0.0 90.3 180.5 270.8 daily change Germany-SK Hof new cases (rolling 7d mean) Germany-SK Hof new cases 0.000 2.256 4.513 6.769 daily change Germany-SK Hof new deaths (rolling 7d mean) Germany-SK Hof new deaths 0.0 35.7 71.4 107.1 142.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Hof", dates="2020-03-15:");
2023-03-07T12:01:56.495208 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: SK Hof SK Hof Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people SK Hof Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Hof")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Hof")
print(f'Population of country="Germany", subregion="SK Hof": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table
Population of country="Germany", subregion="SK Hof": 45125 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 23364 4 196 0
2023-03-03 23360 5 196 0
2023-03-02 23355 3 196 0
2023-03-01 23352 15 196 0
2023-02-28 23337 11 196 0
2023-02-27 23326 11 196 1
2023-02-24 23315 9 195 0
2023-02-23 23306 6 195 0
2023-02-22 23300 10 195 0
2023-02-21 23290 10 195 0
2023-02-20 23280 16 195 0
2023-02-17 23264 7 195 0
2023-02-16 23257 7 195 0
2023-02-15 23250 14 195 1
2023-02-14 23236 11 194 0
2023-02-13 23225 12 194 1
2023-02-10 23213 9 193 0
2023-02-09 23204 7 193 0
2023-02-08 23197 1 193 0
2023-02-07 23196 15 193 0
2023-02-06 23181 13 193 1
2023-02-03 23168 8 192 0
2023-02-02 23160 2 192 0
2023-02-01 23158 10 192 0
2023-01-31 23148 14 192 0
2023-01-30 23134 11 192 0
2023-01-27 23123 7 192 0
2023-01-26 23116 4 192 0
2023-01-25 23112 7 192 0
2023-01-24 23105 8 192 0
2023-01-23 23097 8 192 0
2023-01-20 23089 2 192 0
2023-01-19 23087 4 192 0
2023-01-18 23083 7 192 0
2023-01-16 23076 7 192 0
2023-01-13 23069 2 192 0
2023-01-12 23067 3 192 0
2023-01-11 23064 5 192 0
2023-01-10 23059 15 192 0
2023-01-09 23044 19 192 0
2023-01-05 23025 5 192 0
2023-01-04 23020 6 192 0
2023-01-03 23014 9 192 0
2023-01-02 23005 8 192 1
2022-12-30 22997 17 191 1
2022-12-29 22980 7 190 0
2022-12-28 22973 18 190 0
2022-12-27 22955 22 190 0
2022-12-23 22933 7 190 0
2022-12-22 22926 12 190 0
2022-12-21 22914 18 190 0
2022-12-20 22896 4 190 0
2022-12-19 22892 10 190 0
2022-12-16 22882 8 190 0
2022-12-15 22874 13 190 0
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2022-12-13 22852 13 190 0
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2022-12-07 22820 17 190 0
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2022-12-02 22780 8 190 0
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2022-10-11 21768 58 186 1
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2021-01-10 1591 20 62 0
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2021-01-03 1477 13 62 1
2021-01-02 1464 33 61 6
2021-01-01 1431 18 55 0
2020-12-31 1413 29 55 2
2020-12-30 1384 21 53 2
2020-12-29 1363 11 51 0
2020-12-28 1352 3 51 0
2020-12-27 1349 7 51 1
2020-12-26 1342 18 50 0
2020-12-25 1324 25 50 2
2020-12-24 1299 35 48 1
2020-12-23 1264 23 47 1
2020-12-22 1241 27 46 4
2020-12-21 1214 9 42 1
2020-12-20 1205 13 41 0
2020-12-19 1192 51 41 9
2020-12-18 1141 36 32 0
2020-12-17 1105 26 32 0
2020-12-16 1079 23 32 1
2020-12-15 1056 18 31 1
2020-12-14 1038 26 30 0
2020-12-13 1012 23 30 1
2020-12-12 989 28 29 1
2020-12-11 961 27 28 2
2020-12-10 934 30 26 0
2020-12-09 904 42 26 3
2020-12-08 862 13 23 0
2020-12-07 849 8 23 0
2020-12-06 841 43 23 2
2020-12-05 798 46 21 2
2020-12-04 752 19 19 2
2020-12-03 733 23 17 0
2020-12-02 710 31 17 0
2020-12-01 679 4 17 0
2020-11-30 675 4 17 1
2020-11-29 671 16 16 0
2020-11-28 655 10 16 3
2020-11-27 645 19 13 0
2020-11-26 626 42 13 2
2020-11-25 584 15 11 0
2020-11-24 569 20 11 0
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2020-11-22 545 26 11 0
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2020-11-17 421 5 7 0
2020-11-15 416 9 7 1
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2020-11-13 396 7 6 0
2020-11-12 389 20 6 0
2020-11-11 369 26 6 0
2020-11-10 343 5 6 0
2020-11-09 338 6 6 0
2020-11-08 332 9 6 0
2020-11-07 323 5 6 0
2020-11-06 318 22 6 0
2020-11-05 296 10 6 0
2020-11-04 286 3 6 1
2020-11-03 283 5 5 0
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2020-09-17 187 2 5 0
2020-09-16 185 2 5 0
2020-09-15 183 1 5 0
2020-09-14 182 1 5 0
2020-09-13 181 1 5 0
2020-09-11 180 4 5 0
2020-09-05 176 3 5 0
2020-09-04 173 4 5 0
2020-09-03 169 1 5 0
2020-09-02 168 1 5 0
2020-09-01 167 1 5 0
2020-08-31 166 1 5 0
2020-08-25 165 1 5 0
2020-08-22 164 1 5 0
2020-08-20 163 3 5 0
2020-08-19 160 2 5 0
2020-08-18 158 4 5 0
2020-08-16 154 1 5 1
2020-08-12 153 2 4 0
2020-08-09 151 2 4 0
2020-07-23 149 1 4 0
2020-06-01 148 1 4 0
2020-05-25 147 1 4 0
2020-05-23 146 1 4 0
2020-05-22 145 4 4 0
2020-05-21 141 4 4 0
2020-05-16 137 1 4 0
2020-05-15 136 1 4 0
2020-05-12 135 1 4 0
2020-05-11 134 1 4 0
2020-05-09 133 1 4 0
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2020-05-06 130 1 4 0
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2020-04-30 124 1 4 0
2020-04-29 123 2 4 0
2020-04-28 121 1 4 0
2020-04-27 120 1 4 1
2020-04-26 119 1 3 0
2020-04-25 118 1 3 0
2020-04-24 117 1 3 0
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2020-04-19 108 2 2 0
2020-04-18 106 1 2 0
2020-04-17 105 2 2 0
2020-04-16 103 1 2 0
2020-04-15 102 3 2 0
2020-04-13 99 1 2 0
2020-04-12 98 7 2 0
2020-04-11 91 3 2 0
2020-04-10 88 4 2 0
2020-04-09 84 5 2 1
2020-04-08 79 2 1 0
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2020-04-04 71 4 1 0
2020-04-03 67 9 1 0
2020-04-02 58 5 1 0
2020-04-01 53 5 1 0
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2020-03-30 45 7 1 0
2020-03-29 38 9 1 1
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2020-03-23 16 3 0 0
2020-03-21 13 2 0 0
2020-03-20 11 3 0 0
2020-03-18 8 3 0 0
2020-03-17 5 1 0 0
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2020-03-15 3 2 0 0

Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)

In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")
Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")
Notebook execution took: 0:04:08.582251