Germany: SK Cottbus (Brandenburg)¶

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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 12:20:55 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Cottbus", weeks=5);
2023-03-07T12:21:10.525431 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 58.0 SK Cottbus, 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 0.00 0.25 0.50 0.75 1.00 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 Cottbus cases daily growth factor Germany-SK Cottbus cases daily growth factor (rolling mean) Germany-SK Cottbus 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 Cottbus deaths daily growth factor Germany-SK Cottbus deaths daily growth factor (rolling mean) Germany-SK Cottbus estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-SK Cottbus doubling time cases (rolling mean) 0.00 9.84 19.67 29.51 daily change Germany-SK Cottbus new cases (rolling 7d mean) Germany-SK Cottbus new cases 0.000 0.246 0.492 0.738 0.984 daily change Germany-SK Cottbus new deaths (rolling 7d mean) Germany-SK Cottbus new deaths 0.000 0.264 0.529 0.793
In [4]:
overview(country="Germany", subregion="SK Cottbus");
2023-03-07T12:21:28.248575 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 7-day incidence rate (per 100K people) 58.0 SK Cottbus, 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 2 4 6 8 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 Cottbus cases daily growth factor Germany-SK Cottbus cases daily growth factor (rolling mean) Germany-SK Cottbus 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 Cottbus deaths daily growth factor Germany-SK Cottbus deaths daily growth factor (rolling mean) Germany-SK Cottbus 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 Cottbus doubling time cases (rolling mean) Germany-SK Cottbus doubling time deaths (rolling mean) 0.0 196.7 393.4 590.2 daily change Germany-SK Cottbus new cases (rolling 7d mean) Germany-SK Cottbus new cases 0.000 1.967 3.934 5.902 7.869 daily change Germany-SK Cottbus new deaths (rolling 7d mean) Germany-SK Cottbus new deaths 0.0 58.2 116.4 174.6 232.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Cottbus", dates="2020-03-15:");
2023-03-07T12:23:46.263779 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 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 Cottbus SK Cottbus 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 Cottbus Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Cottbus")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Cottbus")
print(f'Population of country="Germany", subregion="SK Cottbus": {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 Cottbus": 98359 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 50893 7 341 0
2023-03-03 50886 2 341 0
2023-03-02 50884 6 341 0
2023-03-01 50878 13 341 0
2023-02-28 50865 29 341 0
2023-02-27 50836 27 341 1
2023-02-24 50809 13 340 0
2023-02-23 50796 20 340 0
2023-02-22 50776 14 340 0
2023-02-21 50762 31 340 0
2023-02-20 50731 28 340 0
2023-02-17 50703 5 340 0
2023-02-16 50698 22 340 0
2023-02-15 50676 17 340 0
2023-02-14 50659 23 340 0
2023-02-13 50636 24 340 0
2023-02-10 50612 14 340 0
2023-02-09 50598 24 340 0
2023-02-08 50574 19 340 0
2023-02-07 50555 38 340 0
2023-02-06 50517 21 340 1
2023-02-03 50496 12 339 0
2023-02-02 50484 14 339 0
2023-02-01 50470 17 339 0
2023-01-31 50453 21 339 0
2023-01-30 50432 15 339 0
2023-01-27 50417 7 339 0
2023-01-26 50410 4 339 0
2023-01-25 50406 3 339 0
2023-01-24 50403 29 339 0
2023-01-23 50374 9 339 0
2023-01-20 50365 5 339 0
2023-01-19 50360 13 339 0
2023-01-18 50347 10 339 0
2023-01-17 50337 26 339 0
2023-01-16 50311 10 339 0
2023-01-13 50301 9 339 0
2023-01-12 50292 6 339 0
2023-01-11 50286 12 339 0
2023-01-10 50274 30 339 0
2023-01-09 50244 21 339 0
2023-01-06 50223 13 339 0
2023-01-05 50210 21 339 0
2023-01-04 50189 27 339 0
2023-01-03 50162 70 339 0
2023-01-02 50092 61 339 2
2022-12-31 50031 1 337 0
2022-12-30 50030 1 337 0
2022-12-29 50029 59 337 2
2022-12-28 49970 98 335 0
2022-12-27 49872 77 335 0
2022-12-23 49795 33 335 0
2022-12-22 49762 67 335 0
2022-12-21 49695 58 335 0
2022-12-20 49637 113 335 0
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2022-12-13 49256 163 335 1
2022-12-12 49093 91 334 0
2022-12-11 49002 1 334 0
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2021-06-02 5911 1 206 0
2021-06-01 5910 2 206 0
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2021-05-27 5900 5 205 0
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2021-03-11 4431 16 186 0
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2021-02-26 4290 6 184 0
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2021-01-20 3614 50 168 1
2021-01-19 3564 48 167 1
2021-01-18 3516 27 166 1
2021-01-17 3489 54 165 1
2021-01-16 3435 91 164 7
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2021-01-14 3295 32 154 1
2021-01-13 3263 90 153 3
2021-01-12 3173 101 150 4
2021-01-11 3072 56 146 5
2021-01-10 3016 58 141 8
2021-01-09 2958 83 133 1
2021-01-08 2875 105 132 4
2021-01-07 2770 159 128 5
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2021-01-05 2546 80 120 7
2021-01-04 2466 62 113 0
2021-01-02 2404 31 113 1
2021-01-01 2373 28 112 1
2020-12-30 2345 112 111 6
2020-12-29 2233 84 105 3
2020-12-28 2149 111 102 6
2020-12-27 2038 64 96 5
2020-12-26 1974 72 91 4
2020-12-25 1902 54 87 0
2020-12-24 1848 97 87 9
2020-12-23 1751 95 78 4
2020-12-22 1656 86 74 2
2020-12-21 1570 76 72 4
2020-12-20 1494 36 68 0
2020-12-19 1458 75 68 0
2020-12-18 1383 74 68 2
2020-12-17 1309 68 66 0
2020-12-16 1241 42 66 0
2020-12-15 1199 60 66 2
2020-12-11 1139 4 64 1
2020-12-10 1135 3 63 0
2020-12-09 1132 61 63 5
2020-12-08 1071 21 58 3
2020-12-07 1050 40 55 2
2020-12-04 1010 42 53 1
2020-12-03 968 42 52 4
2020-12-02 926 42 48 3
2020-12-01 884 63 45 5
2020-11-30 821 44 40 6
2020-11-27 777 35 34 0
2020-11-26 742 26 34 2
2020-11-24 716 28 32 9
2020-11-23 688 26 23 0
2020-11-21 662 20 23 0
2020-11-19 642 37 23 0
2020-11-18 605 89 23 8
2020-11-17 516 35 15 0
2020-11-16 481 4 15 0
2020-11-14 477 63 15 3
2020-11-13 414 14 12 0
2020-11-10 400 15 12 0
2020-11-07 385 1 12 0
2020-11-05 384 37 12 0
2020-11-02 347 9 12 0
2020-10-31 338 55 12 2
2020-10-27 283 63 10 2
2020-10-25 220 1 8 0
2020-10-24 219 7 8 0
2020-10-23 212 3 8 0
2020-10-22 209 21 8 1
2020-10-21 188 10 7 0
2020-10-20 178 22 7 1
2020-10-19 156 27 6 0
2020-10-16 129 14 6 1
2020-10-15 115 7 5 0
2020-10-14 108 12 5 3
2020-10-13 96 4 2 2
2020-10-12 92 10 0 0
2020-10-09 82 8 0 0
2020-10-08 74 6 0 0
2020-10-07 68 1 0 0
2020-10-06 67 10 0 0
2020-10-03 57 2 0 0
2020-10-02 55 1 0 0
2020-10-01 54 2 0 0
2020-09-29 52 2 0 0
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2020-09-23 49 1 0 0
2020-09-15 48 1 0 0
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2020-09-11 46 1 0 0
2020-08-17 45 1 0 0
2020-08-14 44 1 0 0
2020-07-22 43 4 0 0
2020-04-13 39 4 0 0
2020-04-09 35 1 0 0
2020-04-02 34 2 0 0
2020-04-01 32 1 0 0
2020-03-29 31 2 0 0
2020-03-26 29 1 0 0
2020-03-24 28 1 0 0
2020-03-23 27 3 0 0
2020-03-22 24 4 0 0
2020-03-19 20 3 0 0
2020-03-17 17 1 0 0
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2020-03-14 13 1 0 0
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2020-03-12 8 5 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: 0:03:04.985836