Germany: LK Rotenburg (Wümme) (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 13:22:51 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rotenburg (Wümme)", weeks=5);
2023-03-07T13:23:05.595565 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 80 100 100 120 120 7-day incidence rate (per 100K people) 29.1 LK Rotenburg (Wümme), 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 0.00 0.25 0.50 0.75 1.00 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 Rotenburg (Wümme) cases daily growth factor Germany-LK Rotenburg (Wümme) cases daily growth factor (rolling mean) Germany-LK Rotenburg (Wümme) estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Rotenburg (Wümme) deaths daily growth factor Germany-LK Rotenburg (Wümme) deaths daily growth factor (rolling mean) Germany-LK Rotenburg (Wümme) 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 Rotenburg (Wümme) doubling time cases (rolling mean) 0.0 16.5 33.0 49.5 daily change Germany-LK Rotenburg (Wümme) new cases (rolling 7d mean) Germany-LK Rotenburg (Wümme) new cases 0.000 0.413 0.825 1.238 1.650 daily change Germany-LK Rotenburg (Wümme) new deaths (rolling 7d mean) Germany-LK Rotenburg (Wümme) new deaths 0.000 0.202 0.403 0.605 0.806
In [4]:
overview(country="Germany", subregion="LK Rotenburg (Wümme)");
2023-03-07T13:23:23.567058 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) 29.1 LK Rotenburg (Wümme), 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 6 8 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 Rotenburg (Wümme) cases daily growth factor Germany-LK Rotenburg (Wümme) cases daily growth factor (rolling mean) Germany-LK Rotenburg (Wümme) estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Rotenburg (Wümme) deaths daily growth factor Germany-LK Rotenburg (Wümme) deaths daily growth factor (rolling mean) Germany-LK Rotenburg (Wümme) 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 Rotenburg (Wümme) doubling time cases (rolling mean) Germany-LK Rotenburg (Wümme) doubling time deaths (rolling mean) 0 330 660 daily change Germany-LK Rotenburg (Wümme) new cases (rolling 7d mean) Germany-LK Rotenburg (Wümme) new cases 0.0 3.3 6.6 9.9 13.2 daily change Germany-LK Rotenburg (Wümme) new deaths (rolling 7d mean) Germany-LK Rotenburg (Wümme) new deaths 0.0 61.0 122.0 182.9 243.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rotenburg (Wümme)", dates="2020-03-15:");
2023-03-07T13:25:50.501631 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 Rotenburg (Wümme) LK Rotenburg (Wümme) 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 Rotenburg (Wümme) Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rotenburg (Wümme)")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rotenburg (Wümme)")
print(f'Population of country="Germany", subregion="LK Rotenburg (Wümme)": {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 Rotenburg (Wümme)": 165001 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 75837 1 299 0
2023-03-04 75836 4 299 0
2023-03-03 75832 3 299 0
2023-03-02 75829 5 299 0
2023-03-01 75824 14 299 0
2023-02-28 75810 21 299 0
2023-02-27 75789 11 299 0
2023-02-26 75778 10 299 0
2023-02-25 75768 13 299 0
2023-02-24 75755 10 299 0
2023-02-23 75745 21 299 0
2023-02-22 75724 23 299 0
2023-02-21 75701 41 299 1
2023-02-20 75660 15 298 0
2023-02-19 75645 2 298 0
2023-02-18 75643 6 298 0
2023-02-17 75637 23 298 0
2023-02-16 75614 19 298 0
2023-02-15 75595 28 298 0
2023-02-14 75567 8 298 1
2023-02-13 75559 30 297 2
2023-02-12 75529 2 295 0
2023-02-11 75527 5 295 0
2023-02-10 75522 11 295 0
2023-02-09 75511 13 295 0
2023-02-08 75498 10 295 0
2023-02-07 75488 23 295 0
2023-02-06 75465 20 295 0
2023-02-05 75445 2 295 0
2023-02-04 75443 2 295 0
2023-02-03 75441 12 295 0
2023-02-02 75429 21 295 0
2023-02-01 75408 29 295 0
2023-01-31 75379 62 295 1
2023-01-30 75317 18 294 0
2023-01-29 75299 6 294 0
2023-01-28 75293 9 294 0
2023-01-27 75284 37 294 0
2023-01-26 75247 18 294 0
2023-01-25 75229 47 294 0
2023-01-24 75182 48 294 0
2023-01-23 75134 36 294 1
2023-01-22 75098 11 293 1
2023-01-21 75087 11 292 0
2023-01-20 75076 35 292 0
2023-01-19 75041 19 292 0
2023-01-18 75022 25 292 0
2023-01-17 74997 32 292 0
2023-01-16 74965 44 292 0
2023-01-15 74921 9 292 0
2023-01-14 74912 17 292 1
2023-01-13 74895 10 291 1
2023-01-12 74885 40 290 0
2023-01-11 74845 59 290 2
2023-01-10 74786 47 288 0
2023-01-09 74739 31 288 0
2023-01-08 74708 11 288 1
2023-01-07 74697 18 287 0
2023-01-06 74679 34 287 0
2023-01-05 74645 69 287 0
2023-01-04 74576 51 287 0
2023-01-03 74525 153 287 0
2023-01-02 74372 56 287 1
2023-01-01 74316 22 286 1
2022-12-31 74294 52 285 0
2022-12-30 74242 68 285 0
2022-12-29 74174 47 285 1
2022-12-28 74127 203 284 0
2022-12-27 73924 82 284 1
2022-12-26 73842 21 283 0
2022-12-25 73821 5 283 2
2022-12-24 73816 37 281 0
2022-12-23 73779 94 281 0
2022-12-22 73685 210 281 5
2022-12-21 73475 100 276 0
2022-12-20 73375 258 276 1
2022-12-19 73117 61 275 3
2022-12-18 73056 26 272 0
2022-12-17 73030 26 272 0
2022-12-16 73004 130 272 0
2022-12-15 72874 146 272 1
2022-12-14 72728 153 271 0
2022-12-13 72575 115 271 1
2022-12-12 72460 130 270 1
2022-12-11 72330 20 269 0
2022-12-10 72310 85 269 0
2022-12-09 72225 104 269 0
2022-12-08 72121 187 269 1
2022-12-07 71934 87 268 1
2022-12-06 71847 188 267 1
2022-12-05 71659 118 266 0
2022-12-04 71541 33 266 0
2022-12-03 71508 63 266 0
2022-12-02 71445 107 266 1
2022-12-01 71338 60 265 0
2022-11-30 71278 130 265 0
2022-11-29 71148 142 265 2
2022-11-28 71006 63 263 2
2022-11-27 70943 13 261 1
2022-11-26 70930 56 260 0
2022-11-25 70874 60 260 0
2022-11-24 70814 77 260 0
2022-11-23 70737 33 260 0
2022-11-22 70704 89 260 0
2022-11-21 70615 85 260 2
2022-11-20 70530 25 258 2
2022-11-19 70505 45 256 0
2022-11-18 70460 49 256 0
2022-11-17 70411 59 256 0
2022-11-16 70352 39 256 0
2022-11-15 70313 96 256 0
2022-11-14 70217 84 256 0
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2022-11-12 70116 27 256 0
2022-11-11 70089 53 256 0
2022-11-10 70036 73 256 0
2022-11-09 69963 88 256 1
2022-11-08 69875 80 255 0
2022-11-07 69795 115 255 1
2022-11-06 69680 26 254 0
2022-11-05 69654 57 254 0
2022-11-04 69597 87 254 0
2022-11-03 69510 145 254 1
2022-11-02 69365 186 253 0
2022-11-01 69179 41 253 0
2022-10-31 69138 16 253 1
2022-10-30 69122 46 252 0
2022-10-29 69076 23 252 0
2022-10-28 69053 117 252 1
2022-10-27 68936 116 251 0
2022-10-26 68820 136 251 0
2022-10-25 68684 196 251 3
2022-10-24 68488 156 248 1
2022-10-23 68332 14 247 1
2022-10-22 68318 63 246 1
2022-10-21 68255 128 245 0
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2022-10-16 67379 72 242 0
2022-10-15 67307 48 242 1
2022-10-14 67259 205 241 0
2022-10-13 67054 157 241 1
2022-10-12 66897 312 240 3
2022-10-11 66585 272 237 0
2022-10-10 66313 191 237 0
2022-10-09 66122 58 237 0
2022-10-08 66064 186 237 1
2022-10-07 65878 226 236 0
2022-10-06 65652 332 236 2
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2022-10-04 64954 64 231 0
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Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder.ipynb)
  • 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: 0:04:01.860278