Germany: LK Verden (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 13:30:57 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Verden", weeks=5);
2023-03-07T13:31:07.108970 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 7-day incidence rate (per 100K people) 36.1 LK Verden, 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 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 R & growth factor (based on cases) Germany-LK Verden cases daily growth factor Germany-LK Verden cases daily growth factor (rolling mean) Germany-LK Verden estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Verden deaths daily growth factor Germany-LK Verden deaths daily growth factor (rolling mean) Germany-LK Verden estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Verden doubling time cases (rolling mean) 0.00 13.85 27.70 41.55 daily change Germany-LK Verden new cases (rolling 7d mean) Germany-LK Verden new cases 0.000 0.277 0.554 0.831 daily change Germany-LK Verden new deaths (rolling 7d mean) Germany-LK Verden new deaths 0.000 0.239 0.479 0.718 0.958
In [4]:
overview(country="Germany", subregion="LK Verden");
2023-03-07T13:31:21.848070 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 7-day incidence rate (per 100K people) 36.1 LK Verden, 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 0 100 200 300 400 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 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 R & growth factor (based on cases) Germany-LK Verden cases daily growth factor Germany-LK Verden cases daily growth factor (rolling mean) Germany-LK Verden estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Verden deaths daily growth factor Germany-LK Verden deaths daily growth factor (rolling mean) Germany-LK Verden 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 Verden doubling time cases (rolling mean) Germany-LK Verden doubling time deaths (rolling mean) 0.0 138.5 277.0 415.5 554.0 daily change Germany-LK Verden new cases (rolling 7d mean) Germany-LK Verden new cases 0.000 1.385 2.770 daily change Germany-LK Verden new deaths (rolling 7d mean) Germany-LK Verden new deaths 0 55 110 165 220 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Verden", dates="2020-03-15:");
2023-03-07T13:33:46.283090 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 Verden LK Verden 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 Verden Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Verden")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Verden")
print(f'Population of country="Germany", subregion="LK Verden": {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 Verden": 138507 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 64653 4 215 0
2023-03-03 64649 1 215 1
2023-03-02 64648 12 214 0
2023-03-01 64636 10 214 0
2023-02-28 64626 23 214 0
2023-02-27 64603 17 214 0
2023-02-24 64586 17 214 0
2023-02-23 64569 17 214 0
2023-02-22 64552 12 214 1
2023-02-21 64540 53 213 0
2023-02-20 64487 27 213 0
2023-02-17 64460 6 213 0
2023-02-16 64454 37 213 1
2023-02-15 64417 29 212 0
2023-02-14 64388 21 212 0
2023-02-13 64367 20 212 0
2023-02-10 64347 11 212 0
2023-02-09 64336 20 212 0
2023-02-08 64316 15 212 0
2023-02-07 64301 23 212 0
2023-02-06 64278 28 212 0
2023-02-03 64250 19 212 0
2023-02-02 64231 20 212 0
2023-02-01 64211 36 212 0
2023-01-31 64175 43 212 0
2023-01-30 64132 27 212 0
2023-01-27 64105 10 212 0
2023-01-26 64095 17 212 0
2023-01-25 64078 27 212 0
2023-01-24 64051 30 212 0
2023-01-23 64021 25 212 0
2023-01-20 63996 14 212 0
2023-01-19 63982 23 212 0
2023-01-18 63959 17 212 0
2023-01-17 63942 35 212 0
2023-01-16 63907 24 212 0
2023-01-13 63883 15 212 0
2023-01-12 63868 30 212 0
2023-01-11 63838 33 212 0
2023-01-10 63805 49 212 1
2023-01-09 63756 59 211 0
2023-01-06 63697 40 211 0
2023-01-05 63657 26 211 0
2023-01-04 63631 48 211 0
2023-01-03 63583 71 211 0
2023-01-02 63512 129 211 0
2022-12-30 63383 42 211 0
2022-12-29 63341 104 211 1
2022-12-28 63237 208 210 0
2022-12-27 63029 216 210 1
2022-12-23 62813 32 209 0
2022-12-22 62781 148 209 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: 0:03:06.039877