Germany: SK Braunschweig (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 13:53:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Braunschweig", weeks=5);
2023-03-07T13:53:55.902623 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 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 54.7 SK Braunschweig, 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 0.1 0.2 0.3 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-SK Braunschweig cases daily growth factor Germany-SK Braunschweig cases daily growth factor (rolling mean) Germany-SK Braunschweig estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Braunschweig deaths daily growth factor Germany-SK Braunschweig deaths daily growth factor (rolling mean) Germany-SK Braunschweig 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-SK Braunschweig doubling time cases (rolling mean) 0.00 24.88 49.76 74.65 daily change Germany-SK Braunschweig new cases (rolling 7d mean) Germany-SK Braunschweig new cases 0.000 0.249 0.498 0.746 0.995 daily change Germany-SK Braunschweig new deaths (rolling 7d mean) Germany-SK Braunschweig new deaths 0.000 0.302 0.603 0.905
In [4]:
overview(country="Germany", subregion="SK Braunschweig");
2023-03-07T13:54:13.640608 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 7-day incidence rate (per 100K people) 54.7 SK Braunschweig, 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 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-SK Braunschweig cases daily growth factor Germany-SK Braunschweig cases daily growth factor (rolling mean) Germany-SK Braunschweig estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Braunschweig deaths daily growth factor Germany-SK Braunschweig deaths daily growth factor (rolling mean) Germany-SK Braunschweig 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-SK Braunschweig doubling time cases (rolling mean) Germany-SK Braunschweig doubling time deaths (rolling mean) 0 498 995 1493 1991 daily change Germany-SK Braunschweig new cases (rolling 7d mean) Germany-SK Braunschweig new cases 0.00 2.49 4.98 7.46 9.95 daily change Germany-SK Braunschweig new deaths (rolling 7d mean) Germany-SK Braunschweig new deaths 0.0 191.3 382.6 573.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Braunschweig", dates="2020-03-15:");
2023-03-07T13:56:15.156846 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: SK Braunschweig SK Braunschweig 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 SK Braunschweig Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Braunschweig")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Braunschweig")
print(f'Population of country="Germany", subregion="SK Braunschweig": {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="SK Braunschweig": 248823 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 110442 22 494 0
2023-03-03 110420 10 494 0
2023-03-02 110410 18 494 0
2023-03-01 110392 32 494 0
2023-02-28 110360 54 494 0
2023-02-27 110306 58 494 0
2023-02-24 110248 28 494 0
2023-02-23 110220 52 494 0
2023-02-22 110168 63 494 0
2023-02-21 110105 60 494 0
2023-02-20 110045 58 494 1
2023-02-17 109987 28 493 0
2023-02-16 109959 48 493 0
2023-02-15 109911 62 493 0
2023-02-14 109849 80 493 0
2023-02-13 109769 65 493 1
2023-02-10 109704 33 492 0
2023-02-09 109671 34 492 0
2023-02-08 109637 57 492 1
2023-02-07 109580 45 491 1
2023-02-06 109535 44 490 0
2023-02-03 109491 34 490 0
2023-02-02 109457 22 490 1
2023-02-01 109435 99 489 1
2023-01-31 109336 93 488 1
2023-01-30 109243 62 487 1
2023-01-27 109181 52 486 0
2023-01-26 109129 50 486 1
2023-01-25 109079 59 485 1
2023-01-24 109020 95 484 0
2023-01-23 108925 78 484 1
2023-01-20 108847 49 483 1
2023-01-19 108798 59 482 0
2023-01-18 108739 49 482 0
2023-01-17 108690 60 482 0
2023-01-16 108630 63 482 1
2023-01-13 108567 43 481 0
2023-01-12 108524 63 481 1
2023-01-11 108461 62 480 1
2023-01-10 108399 105 479 1
2023-01-09 108294 121 478 0
2023-01-06 108173 79 478 2
2023-01-05 108094 97 476 1
2023-01-04 107997 102 475 0
2023-01-03 107895 177 475 3
2023-01-02 107718 165 472 0
2022-12-30 107553 110 472 0
2022-12-29 107443 185 472 1
2022-12-28 107258 243 471 1
2022-12-27 107015 251 470 2
2022-12-23 106764 169 468 1
2022-12-22 106595 236 467 0
2022-12-21 106359 217 467 3
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2022-12-09 104517 164 453 2
2022-12-08 104353 210 451 2
2022-12-07 104143 212 449 0
2022-12-06 103931 305 449 2
2022-12-05 103626 174 447 1
2022-12-02 103452 153 446 2
2022-12-01 103299 177 444 0
2022-11-30 103122 225 444 1
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2022-11-25 102382 118 437 0
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2022-10-28 98985 165 417 1
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2022-10-26 98597 240 415 0
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2022-10-24 97971 305 415 2
2022-10-21 97666 206 413 1
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2022-10-18 96807 468 404 1
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2022-10-14 95914 263 401 0
2022-10-13 95651 345 401 1
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2022-10-06 93733 443 394 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: 0:02:46.138496