Germany: LK Barnim (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 12:13:48 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Barnim", weeks=5);
2023-03-07T12:14:11.357868 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 70 70 7-day incidence rate (per 100K people) 37.6 LK Barnim, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 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-LK Barnim cases daily growth factor Germany-LK Barnim cases daily growth factor (rolling mean) Germany-LK Barnim 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-LK Barnim deaths daily growth factor Germany-LK Barnim deaths daily growth factor (rolling mean) Germany-LK Barnim 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-LK Barnim doubling time cases (rolling mean) 0.00 9.44 18.88 28.33 37.77 daily change Germany-LK Barnim new cases (rolling 7d mean) Germany-LK Barnim new cases 0.000 0.378 0.755 daily change Germany-LK Barnim new deaths (rolling 7d mean) Germany-LK Barnim new deaths 0.000 0.258 0.516 0.775
In [4]:
overview(country="Germany", subregion="LK Barnim");
2023-03-07T12:14:31.665620 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) 37.6 LK Barnim, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1 2 3 4 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-LK Barnim cases daily growth factor Germany-LK Barnim cases daily growth factor (rolling mean) Germany-LK Barnim 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-LK Barnim deaths daily growth factor Germany-LK Barnim deaths daily growth factor (rolling mean) Germany-LK Barnim 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-LK Barnim doubling time cases (rolling mean) Germany-LK Barnim doubling time deaths (rolling mean) 0 378 755 daily change Germany-LK Barnim new cases (rolling 7d mean) Germany-LK Barnim new cases 0.000 1.888 3.777 5.665 7.553 daily change Germany-LK Barnim new deaths (rolling 7d mean) Germany-LK Barnim new deaths 0.0 85.6 171.2 256.9 342.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Barnim", dates="2020-03-15:");
2023-03-07T12:16:43.056025 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: LK Barnim LK Barnim 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 LK Barnim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Barnim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Barnim")
print(f'Population of country="Germany", subregion="LK Barnim": {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="LK Barnim": 188835 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 69317 6 400 0
2023-03-02 69311 10 400 0
2023-03-01 69301 22 400 0
2023-02-28 69279 33 400 0
2023-02-27 69246 19 400 0
2023-02-24 69227 30 400 0
2023-02-23 69197 5 400 0
2023-02-22 69192 12 400 0
2023-02-21 69180 45 400 0
2023-02-20 69135 24 400 1
2023-02-17 69111 14 399 0
2023-02-16 69097 27 399 0
2023-02-15 69070 21 399 0
2023-02-14 69049 24 399 0
2023-02-13 69025 27 399 0
2023-02-10 68998 15 399 0
2023-02-09 68983 31 399 0
2023-02-08 68952 11 399 0
2023-02-07 68941 44 399 0
2023-02-06 68897 28 399 0
2023-02-03 68869 20 399 1
2023-02-02 68849 12 398 0
2023-02-01 68837 14 398 0
2023-01-31 68823 26 398 0
2023-01-30 68797 51 398 0
2023-01-27 68746 7 398 0
2023-01-26 68739 14 398 0
2023-01-25 68725 9 398 0
2023-01-24 68716 14 398 0
2023-01-23 68702 24 398 0
2023-01-20 68678 13 398 0
2023-01-19 68665 9 398 0
2023-01-18 68656 10 398 0
2023-01-17 68646 29 398 0
2023-01-16 68617 31 398 1
2023-01-13 68586 10 397 0
2023-01-12 68576 28 397 1
2023-01-11 68548 10 396 0
2023-01-10 68538 39 396 1
2023-01-09 68499 50 395 0
2023-01-06 68449 24 395 0
2023-01-05 68425 30 395 0
2023-01-04 68395 66 395 0
2023-01-03 68329 187 395 0
2023-01-02 68142 196 395 0
2022-12-30 67946 15 395 0
2022-12-29 67931 23 395 0
2022-12-28 67908 44 395 1
2022-12-27 67864 38 394 2
2022-12-23 67826 33 392 0
2022-12-22 67793 80 392 0
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2022-12-20 67574 104 392 3
2022-12-19 67470 104 389 0
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2022-12-14 67157 121 389 2
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2022-12-09 66778 58 383 0
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2022-12-06 66513 143 383 2
2022-12-05 66370 72 381 1
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2022-11-30 66156 84 379 1
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2022-10-23 62830 2 370 0
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2022-10-20 62708 216 370 2
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2022-10-06 60803 158 362 1
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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: 0:03:10.093408