Germany: LK Vechta (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 13:30:46 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Vechta", weeks=5);
2023-03-07T13:30:59.032573 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 200 200 7-day incidence rate (per 100K people) 54.6 LK Vechta, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Vechta new deaths (rolling 7d mean) Germany-LK Vechta new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Vechta cases daily growth factor Germany-LK Vechta cases daily growth factor (rolling mean) Germany-LK Vechta estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Vechta deaths daily growth factor Germany-LK Vechta deaths daily growth factor (rolling mean) Germany-LK Vechta 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 Vechta doubling time cases (rolling mean) 0.0 29.0 57.9 86.9 daily change Germany-LK Vechta new cases (rolling 7d mean) Germany-LK Vechta new cases 0.000 0.229 0.458 0.686 0.915
In [4]:
overview(country="Germany", subregion="LK Vechta");
2023-03-07T13:31:14.569856 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 4000 4000 7-day incidence rate (per 100K people) 54.6 LK Vechta, 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 0 200 400 600 800 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 R & growth factor (based on cases) Germany-LK Vechta cases daily growth factor Germany-LK Vechta cases daily growth factor (rolling mean) Germany-LK Vechta estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Vechta deaths daily growth factor Germany-LK Vechta deaths daily growth factor (rolling mean) Germany-LK Vechta estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Vechta doubling time cases (rolling mean) Germany-LK Vechta doubling time deaths (rolling mean) 0 290 579 869 1158 daily change Germany-LK Vechta new cases (rolling 7d mean) Germany-LK Vechta new cases 0.000 1.448 2.896 4.344 daily change Germany-LK Vechta new deaths (rolling 7d mean) Germany-LK Vechta new deaths 0.0 25.2 50.4 75.6 100.7 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Vechta", dates="2020-03-15:");
2023-03-07T13:33:41.539915 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 Vechta LK Vechta 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 Vechta Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Vechta")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Vechta")
print(f'Population of country="Germany", subregion="LK Vechta": {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 Vechta": 144805 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 90545 1 139 0
2023-03-05 90544 4 139 0
2023-03-04 90540 8 139 0
2023-03-03 90532 6 139 0
2023-03-02 90526 10 139 0
2023-03-01 90516 22 139 0
2023-02-28 90494 28 139 0
2023-02-27 90466 38 139 0
2023-02-26 90428 7 139 0
2023-02-25 90421 13 139 0
2023-02-24 90408 32 139 0
2023-02-23 90376 34 139 0
2023-02-22 90342 36 139 0
2023-02-21 90306 51 139 0
2023-02-20 90255 76 139 0
2023-02-19 90179 2 139 0
2023-02-18 90177 34 139 0
2023-02-17 90143 60 139 0
2023-02-16 90083 50 139 0
2023-02-15 90033 43 139 0
2023-02-14 89990 28 139 0
2023-02-13 89962 44 139 0
2023-02-12 89918 4 139 0
2023-02-11 89914 13 139 0
2023-02-10 89901 18 139 0
2023-02-09 89883 34 139 0
2023-02-08 89849 25 139 0
2023-02-07 89824 29 139 0
2023-02-06 89795 21 139 0
2023-02-05 89774 1 139 0
2023-02-04 89773 3 139 0
2023-02-03 89770 21 139 0
2023-02-02 89749 26 139 0
2023-02-01 89723 48 139 0
2023-01-31 89675 103 139 0
2023-01-30 89572 48 139 0
2023-01-29 89524 3 139 0
2023-01-28 89521 13 139 0
2023-01-27 89508 31 139 0
2023-01-26 89477 55 139 0
2023-01-25 89422 44 139 0
2023-01-24 89378 49 139 0
2023-01-23 89329 61 139 0
2023-01-22 89268 4 139 0
2023-01-21 89264 12 139 0
2023-01-20 89252 47 139 0
2023-01-19 89205 27 139 0
2023-01-18 89178 15 139 0
2023-01-17 89163 40 139 0
2023-01-16 89123 42 139 0
2023-01-15 89081 3 139 0
2023-01-14 89078 14 139 0
2023-01-13 89064 43 139 0
2023-01-12 89021 35 139 1
2023-01-11 88986 51 138 0
2023-01-10 88935 73 138 0
2023-01-09 88862 59 138 0
2023-01-08 88803 1 138 0
2023-01-07 88802 27 138 0
2023-01-06 88775 86 138 0
2023-01-05 88689 69 138 0
2023-01-04 88620 73 138 0
2023-01-03 88547 107 138 0
2023-01-02 88440 73 138 0
2023-01-01 88367 4 138 0
2022-12-31 88363 36 138 0
2022-12-30 88327 80 138 0
2022-12-29 88247 81 138 0
2022-12-28 88166 107 138 0
2022-12-27 88059 59 138 1
2022-12-26 88000 6 137 0
2022-12-25 87994 3 137 0
2022-12-24 87991 50 137 0
2022-12-23 87941 85 137 0
2022-12-22 87856 92 137 0
2022-12-21 87764 97 137 0
2022-12-20 87667 122 137 0
2022-12-19 87545 116 137 0
2022-12-18 87429 5 137 0
2022-12-17 87424 47 137 0
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2022-12-13 87088 84 134 1
2022-12-12 87004 88 133 0
2022-12-11 86916 7 133 1
2022-12-10 86909 38 132 1
2022-12-09 86871 85 131 0
2022-12-08 86786 48 131 0
2022-12-07 86738 85 131 0
2022-12-06 86653 94 131 1
2022-12-05 86559 91 130 0
2022-12-04 86468 10 130 0
2022-12-03 86458 31 130 0
2022-12-02 86427 79 130 0
2022-12-01 86348 48 130 0
2022-11-30 86300 72 130 1
2022-11-29 86228 85 129 1
2022-11-28 86143 62 128 0
2022-11-27 86081 8 128 0
2022-11-26 86073 40 128 0
2022-11-25 86033 70 128 1
2022-11-24 85963 56 127 0
2022-11-23 85907 100 127 0
2022-11-22 85807 80 127 0
2022-11-21 85727 58 127 0
2022-11-20 85669 3 127 0
2022-11-19 85666 29 127 0
2022-11-18 85637 55 127 0
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2022-11-02 84800 133 126 0
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2022-10-31 84593 9 126 1
2022-10-30 84584 6 125 0
2022-10-29 84578 43 125 0
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2022-10-27 84442 75 125 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: 0:03:15.434150