Germany: LK Schaumburg (Niedersachsen)¶

  • Homepage of project: https://oscovida.github.io
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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 13:30:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Schaumburg", weeks=5);
2023-03-07T13:30:58.414418 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 120 120 140 140 160 160 180 180 7-day incidence rate (per 100K people) 105.0 LK Schaumburg, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Schaumburg cases daily growth factor Germany-LK Schaumburg cases daily growth factor (rolling mean) Germany-LK Schaumburg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 R & growth factor (based on deaths) Germany-LK Schaumburg deaths daily growth factor Germany-LK Schaumburg deaths daily growth factor (rolling mean) Germany-LK Schaumburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Schaumburg doubling time cases (rolling mean) 0.00 31.62 63.24 daily change Germany-LK Schaumburg new cases (rolling 7d mean) Germany-LK Schaumburg new cases 0.000 0.316 0.632 0.949 daily change Germany-LK Schaumburg new deaths (rolling 7d mean) Germany-LK Schaumburg new deaths 0.000 0.261 0.521 0.782
In [4]:
overview(country="Germany", subregion="LK Schaumburg");
2023-03-07T13:31:12.697630 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) 105.0 LK Schaumburg, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Schaumburg cases daily growth factor Germany-LK Schaumburg cases daily growth factor (rolling mean) Germany-LK Schaumburg 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 R & growth factor (based on deaths) Germany-LK Schaumburg deaths daily growth factor Germany-LK Schaumburg deaths daily growth factor (rolling mean) Germany-LK Schaumburg 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 Schaumburg doubling time cases (rolling mean) Germany-LK Schaumburg doubling time deaths (rolling mean) 0 316 632 949 daily change Germany-LK Schaumburg new cases (rolling 7d mean) Germany-LK Schaumburg new cases 0.000 1.581 3.162 4.743 6.324 daily change Germany-LK Schaumburg new deaths (rolling 7d mean) Germany-LK Schaumburg new deaths 0.0 85.1 170.3 255.4 340.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Schaumburg", dates="2020-03-15:");
2023-03-07T13:33:35.716060 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 Schaumburg LK Schaumburg 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 Schaumburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Schaumburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Schaumburg")
print(f'Population of country="Germany", subregion="LK Schaumburg": {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 Schaumburg": 158108 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 79742 15 296 0
2023-03-03 79727 18 296 0
2023-03-02 79709 23 296 0
2023-03-01 79686 27 296 0
2023-02-28 79659 83 296 0
2023-02-27 79576 68 296 0
2023-02-24 79508 31 296 0
2023-02-23 79477 20 296 0
2023-02-22 79457 55 296 0
2023-02-21 79402 74 296 0
2023-02-20 79328 51 296 1
2023-02-17 79277 34 295 0
2023-02-16 79243 27 295 0
2023-02-15 79216 52 295 1
2023-02-14 79164 57 294 0
2023-02-13 79107 50 294 1
2023-02-10 79057 27 293 0
2023-02-09 79030 24 293 1
2023-02-08 79006 34 292 0
2023-02-07 78972 48 292 0
2023-02-06 78924 44 292 0
2023-02-03 78880 30 292 0
2023-02-02 78850 34 292 0
2023-02-01 78816 53 292 0
2023-01-31 78763 69 292 0
2023-01-30 78694 65 292 1
2023-01-27 78629 50 291 0
2023-01-26 78579 53 291 0
2023-01-25 78526 62 291 1
2023-01-24 78464 70 290 1
2023-01-23 78394 48 289 0
2023-01-20 78346 25 289 0
2023-01-19 78321 18 289 0
2023-01-18 78303 45 289 0
2023-01-17 78258 46 289 0
2023-01-16 78212 56 289 0
2023-01-13 78156 35 289 0
2023-01-12 78121 42 289 1
2023-01-11 78079 47 288 0
2023-01-10 78032 86 288 0
2023-01-09 77946 105 288 3
2023-01-06 77841 63 285 0
2023-01-05 77778 73 285 0
2023-01-04 77705 135 285 1
2023-01-03 77570 161 284 1
2023-01-02 77409 149 283 0
2022-12-30 77260 86 283 0
2022-12-29 77174 144 283 0
2022-12-28 77030 236 283 1
2022-12-27 76794 278 282 2
2022-12-25 76516 1 280 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: 0:03:02.325617