Germany: LK Peine (Niedersachsen)¶

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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 13:30:29 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Peine", weeks=5);
2023-03-07T13:30:46.588689 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 7-day incidence rate (per 100K people) 50.4 LK Peine, 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 40 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Peine cases daily growth factor Germany-LK Peine cases daily growth factor (rolling mean) Germany-LK Peine 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 Peine deaths daily growth factor Germany-LK Peine deaths daily growth factor (rolling mean) Germany-LK Peine estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Peine doubling time cases (rolling mean) 0.00 13.70 27.39 41.09 54.78 daily change Germany-LK Peine new cases (rolling 7d mean) Germany-LK Peine new cases 0.000 0.274 0.548 0.822 daily change Germany-LK Peine new deaths (rolling 7d mean) Germany-LK Peine new deaths 0.000 0.247 0.494 0.742 0.989
In [4]:
overview(country="Germany", subregion="LK Peine");
2023-03-07T13:31:03.457585 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) 50.4 LK Peine, 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Peine cases daily growth factor Germany-LK Peine cases daily growth factor (rolling mean) Germany-LK Peine 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 Peine deaths daily growth factor Germany-LK Peine deaths daily growth factor (rolling mean) Germany-LK Peine 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 Peine doubling time cases (rolling mean) Germany-LK Peine doubling time deaths (rolling mean) 0.0 273.9 547.8 daily change Germany-LK Peine new cases (rolling 7d mean) Germany-LK Peine new cases 0.000 1.370 2.739 4.109 5.478 daily change Germany-LK Peine new deaths (rolling 7d mean) Germany-LK Peine new deaths 0.0 34.3 68.6 102.8 137.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Peine", dates="2020-03-15:");
2023-03-07T13:33:30.129962 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 Peine LK Peine 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 Peine Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Peine")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Peine")
print(f'Population of country="Germany", subregion="LK Peine": {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 Peine": 136960 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 70234 2 206 0
2023-03-04 70232 10 206 0
2023-03-03 70222 7 206 0
2023-03-02 70215 4 206 0
2023-03-01 70211 11 206 0
2023-02-28 70200 35 206 0
2023-02-27 70165 25 206 0
2023-02-26 70140 5 206 0
2023-02-25 70135 17 206 0
2023-02-24 70118 37 206 0
2023-02-23 70081 27 206 0
2023-02-22 70054 40 206 0
2023-02-21 70014 61 206 1
2023-02-20 69953 22 205 0
2023-02-19 69931 12 205 0
2023-02-18 69919 9 205 0
2023-02-17 69910 39 205 0
2023-02-16 69871 27 205 0
2023-02-15 69844 38 205 0
2023-02-14 69806 27 205 0
2023-02-13 69779 19 205 0
2023-02-12 69760 14 205 0
2023-02-11 69746 6 205 0
2023-02-10 69740 15 205 0
2023-02-09 69725 17 205 0
2023-02-08 69708 20 205 0
2023-02-07 69688 29 205 0
2023-02-06 69659 32 205 0
2023-02-05 69627 7 205 0
2023-02-04 69620 15 205 0
2023-02-03 69605 25 205 0
2023-02-02 69580 24 205 0
2023-02-01 69556 32 205 0
2023-01-31 69524 65 205 0
2023-01-30 69459 28 205 0
2023-01-29 69431 11 205 0
2023-01-28 69420 16 205 0
2023-01-27 69404 23 205 0
2023-01-26 69381 48 205 0
2023-01-25 69333 40 205 0
2023-01-24 69293 64 205 0
2023-01-23 69229 30 205 1
2023-01-22 69199 10 204 0
2023-01-21 69189 30 204 0
2023-01-20 69159 14 204 0
2023-01-19 69145 27 204 1
2023-01-18 69118 40 203 0
2023-01-17 69078 47 203 0
2023-01-16 69031 40 203 0
2023-01-15 68991 7 203 0
2023-01-14 68984 14 203 0
2023-01-13 68970 18 203 0
2023-01-12 68952 31 203 0
2023-01-11 68921 29 203 0
2023-01-10 68892 133 203 0
2023-01-09 68759 47 203 0
2023-01-08 68712 17 203 0
2023-01-07 68695 29 203 0
2023-01-06 68666 56 203 1
2023-01-05 68610 40 202 0
2023-01-04 68570 73 202 0
2023-01-03 68497 126 202 1
2023-01-02 68371 48 201 0
2023-01-01 68323 14 201 0
2022-12-31 68309 58 201 0
2022-12-30 68251 72 201 1
2022-12-29 68179 81 200 0
2022-12-28 68098 157 200 0
2022-12-27 67941 69 200 1
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2022-12-25 67840 38 199 2
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2022-12-02 65439 76 186 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: 0:03:07.509192