Germany: LK Oberhavel (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 12:13:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Oberhavel", weeks=5);
2023-03-07T12:13:21.801029 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 49.1 LK Oberhavel, 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 0.6 0.8 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 Oberhavel cases daily growth factor Germany-LK Oberhavel cases daily growth factor (rolling mean) Germany-LK Oberhavel 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 Oberhavel deaths daily growth factor Germany-LK Oberhavel deaths daily growth factor (rolling mean) Germany-LK Oberhavel 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 Oberhavel doubling time cases (rolling mean) 0.00 10.79 21.58 32.37 43.16 daily change Germany-LK Oberhavel new cases (rolling 7d mean) Germany-LK Oberhavel new cases 0.000 0.432 0.863 1.295 1.726 daily change Germany-LK Oberhavel new deaths (rolling 7d mean) Germany-LK Oberhavel new deaths 0.000 0.301 0.601 0.902
In [4]:
overview(country="Germany", subregion="LK Oberhavel");
2023-03-07T12:13:40.511943 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 49.1 LK Oberhavel, 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 2 4 6 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 Oberhavel cases daily growth factor Germany-LK Oberhavel cases daily growth factor (rolling mean) Germany-LK Oberhavel 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 Oberhavel deaths daily growth factor Germany-LK Oberhavel deaths daily growth factor (rolling mean) Germany-LK Oberhavel estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 cases doubling time [days] Germany-LK Oberhavel doubling time cases (rolling mean) Germany-LK Oberhavel doubling time deaths (rolling mean) 0 432 863 daily change Germany-LK Oberhavel new cases (rolling 7d mean) Germany-LK Oberhavel new cases 0.00 4.32 8.63 12.95 daily change Germany-LK Oberhavel new deaths (rolling 7d mean) Germany-LK Oberhavel new deaths 0.0 204.2 408.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Oberhavel", dates="2020-03-15:");
2023-03-07T12:16:39.835399 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 Oberhavel LK Oberhavel 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 Oberhavel Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Oberhavel")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Oberhavel")
print(f'Population of country="Germany", subregion="LK Oberhavel": {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 Oberhavel": 215795 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 89046 18 501 0
2023-03-03 89028 15 501 0
2023-03-02 89013 11 501 0
2023-03-01 89002 28 501 0
2023-02-28 88974 34 501 0
2023-02-27 88940 29 501 1
2023-02-24 88911 18 500 0
2023-02-23 88893 18 500 0
2023-02-22 88875 22 500 0
2023-02-21 88853 37 500 0
2023-02-20 88816 20 500 0
2023-02-17 88796 20 500 0
2023-02-16 88776 23 500 0
2023-02-15 88753 42 500 0
2023-02-14 88711 44 500 0
2023-02-13 88667 41 500 0
2023-02-11 88626 1 500 0
2023-02-10 88625 15 500 0
2023-02-09 88610 25 500 0
2023-02-08 88585 32 500 0
2023-02-07 88553 33 500 0
2023-02-06 88520 26 500 0
2023-02-03 88494 18 500 1
2023-02-02 88476 21 499 0
2023-02-01 88455 11 499 0
2023-01-31 88444 47 499 2
2023-01-30 88397 27 497 0
2023-01-27 88370 10 497 0
2023-01-26 88360 19 497 0
2023-01-25 88341 53 497 0
2023-01-24 88288 30 497 0
2023-01-23 88258 22 497 0
2023-01-20 88236 12 497 0
2023-01-19 88224 19 497 0
2023-01-18 88205 12 497 0
2023-01-17 88193 35 497 0
2023-01-16 88158 32 497 0
2023-01-13 88126 14 497 1
2023-01-12 88112 26 496 1
2023-01-11 88086 39 495 0
2023-01-10 88047 71 495 0
2023-01-09 87976 45 495 0
2023-01-06 87931 33 495 0
2023-01-05 87898 31 495 0
2023-01-04 87867 65 495 1
2023-01-03 87802 63 494 0
2023-01-02 87739 147 494 2
2022-12-30 87592 50 492 3
2022-12-29 87542 112 489 3
2022-12-28 87430 181 486 1
2022-12-27 87249 129 485 2
2022-12-23 87120 80 483 0
2022-12-22 87040 85 483 2
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2022-12-19 86655 149 477 1
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2022-12-12 85887 177 473 2
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2022-12-08 85613 142 469 3
2022-12-07 85471 157 466 1
2022-12-06 85314 183 465 0
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2022-12-02 84971 152 465 2
2022-12-01 84819 104 463 1
2022-11-30 84715 110 462 0
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2022-10-11 78637 294 438 0
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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: 0:03:42.242706