Germany: LK Aurich (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 13:07:21 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Aurich", weeks=5);
2023-03-07T13:07:33.888783 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 7-day incidence rate (per 100K people) 47.8 LK Aurich, 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 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 Aurich cases daily growth factor Germany-LK Aurich cases daily growth factor (rolling mean) Germany-LK Aurich 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 Aurich deaths daily growth factor Germany-LK Aurich deaths daily growth factor (rolling mean) Germany-LK Aurich 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 Aurich doubling time cases (rolling mean) 0.00 19.04 38.08 daily change Germany-LK Aurich new cases (rolling 7d mean) Germany-LK Aurich new cases 0.000 0.381 0.762 daily change Germany-LK Aurich new deaths (rolling 7d mean) Germany-LK Aurich new deaths 0.000 0.268 0.537 0.805
In [4]:
overview(country="Germany", subregion="LK Aurich");
2023-03-07T13:07:55.284967 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) 47.8 LK Aurich, 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 1 2 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 Aurich cases daily growth factor Germany-LK Aurich cases daily growth factor (rolling mean) Germany-LK Aurich 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 Aurich deaths daily growth factor Germany-LK Aurich deaths daily growth factor (rolling mean) Germany-LK Aurich 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 cases doubling time [days] Germany-LK Aurich doubling time cases (rolling mean) Germany-LK Aurich doubling time deaths (rolling mean) 0 381 762 1143 daily change Germany-LK Aurich new cases (rolling 7d mean) Germany-LK Aurich new cases 0.000 1.904 3.808 daily change Germany-LK Aurich new deaths (rolling 7d mean) Germany-LK Aurich new deaths 0.0 51.9 103.8 155.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Aurich", dates="2020-03-15:");
2023-03-07T13:10:17.884160 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 Aurich LK Aurich 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 Aurich Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Aurich")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Aurich")
print(f'Population of country="Germany", subregion="LK Aurich": {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 Aurich": 190425 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 89359 6 220 0
2023-03-05 89353 3 220 0
2023-03-04 89350 9 220 0
2023-03-03 89341 16 220 0
2023-03-02 89325 8 220 0
2023-03-01 89317 17 220 0
2023-02-28 89300 32 220 0
2023-02-27 89268 28 220 0
2023-02-26 89240 11 220 0
2023-02-25 89229 5 220 0
2023-02-24 89224 17 220 0
2023-02-23 89207 15 220 0
2023-02-22 89192 34 220 0
2023-02-21 89158 41 220 0
2023-02-20 89117 36 220 0
2023-02-19 89081 9 220 0
2023-02-18 89072 7 220 1
2023-02-17 89065 20 219 0
2023-02-16 89045 29 219 0
2023-02-15 89016 13 219 0
2023-02-14 89003 31 219 0
2023-02-13 88972 37 219 0
2023-02-12 88935 7 219 0
2023-02-11 88928 18 219 0
2023-02-10 88910 20 219 0
2023-02-09 88890 30 219 0
2023-02-08 88860 25 219 0
2023-02-07 88835 27 219 0
2023-02-06 88808 14 219 0
2023-02-05 88794 1 219 0
2023-02-04 88793 9 219 0
2023-02-03 88784 32 219 0
2023-02-02 88752 20 219 0
2023-02-01 88732 40 219 0
2023-01-31 88692 49 219 1
2023-01-30 88643 43 218 0
2023-01-29 88600 7 218 0
2023-01-28 88593 11 218 0
2023-01-27 88582 35 218 0
2023-01-26 88547 28 218 0
2023-01-25 88519 41 218 0
2023-01-24 88478 43 218 0
2023-01-23 88435 46 218 0
2023-01-22 88389 4 218 0
2023-01-21 88385 12 218 0
2023-01-20 88373 23 218 0
2023-01-19 88350 25 218 0
2023-01-18 88325 28 218 1
2023-01-17 88297 67 217 0
2023-01-16 88230 34 217 0
2023-01-15 88196 2 217 0
2023-01-14 88194 13 217 0
2023-01-13 88181 26 217 0
2023-01-12 88155 36 217 0
2023-01-11 88119 47 217 0
2023-01-10 88072 78 217 0
2023-01-09 87994 69 217 0
2023-01-08 87925 7 217 0
2023-01-07 87918 26 217 0
2023-01-06 87892 37 217 0
2023-01-05 87855 85 217 1
2023-01-04 87770 67 216 0
2023-01-03 87703 134 216 0
2023-01-02 87569 124 216 1
2023-01-01 87445 16 215 0
2022-12-31 87429 56 215 3
2022-12-30 87373 96 212 1
2022-12-29 87277 131 211 2
2022-12-28 87146 175 209 2
2022-12-27 86971 118 207 1
2022-12-26 86853 19 206 0
2022-12-25 86834 11 206 0
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2022-12-05 84342 170 193 1
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2022-12-03 84154 55 192 1
2022-12-02 84099 120 191 0
2022-12-01 83979 129 191 0
2022-11-30 83850 136 191 0
2022-11-29 83714 147 191 0
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2020-03-22 28 1 0 0
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2020-03-11 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: 0:03:08.464850