Germany: LK Saarlouis (Saarland)¶

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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 15:11:30 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Saarlouis", weeks=5);
2023-03-07T15:11:43.149863 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 200 200 300 300 7-day incidence rate (per 100K people) 215.3 LK Saarlouis, 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 80 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 Saarlouis new deaths (rolling 7d mean) Germany-LK Saarlouis 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 1.75 1.75 R & growth factor (based on cases) Germany-LK Saarlouis cases daily growth factor Germany-LK Saarlouis cases daily growth factor (rolling mean) Germany-LK Saarlouis 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 Saarlouis deaths daily growth factor Germany-LK Saarlouis deaths daily growth factor (rolling mean) Germany-LK Saarlouis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Saarlouis doubling time cases (rolling mean) 0.0 38.7 77.5 116.2 154.9 daily change Germany-LK Saarlouis new cases (rolling 7d mean) Germany-LK Saarlouis new cases 0.000 0.232 0.463 0.695 0.927
In [4]:
overview(country="Germany", subregion="LK Saarlouis");
2023-03-07T15:12:00.037623 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) 215.3 LK Saarlouis, 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 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Saarlouis cases daily growth factor Germany-LK Saarlouis cases daily growth factor (rolling mean) Germany-LK Saarlouis 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 Saarlouis deaths daily growth factor Germany-LK Saarlouis deaths daily growth factor (rolling mean) Germany-LK Saarlouis 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-LK Saarlouis doubling time cases (rolling mean) Germany-LK Saarlouis doubling time deaths (rolling mean) 0 387 775 daily change Germany-LK Saarlouis new cases (rolling 7d mean) Germany-LK Saarlouis new cases 0.000 1.937 3.873 5.810 7.746 daily change Germany-LK Saarlouis new deaths (rolling 7d mean) Germany-LK Saarlouis new deaths 0 73 146 219 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Saarlouis", dates="2020-03-15:");
2023-03-07T15:15:04.180920 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 Saarlouis LK Saarlouis 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 Saarlouis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Saarlouis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Saarlouis")
print(f'Population of country="Germany", subregion="LK Saarlouis": {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 Saarlouis": 193661 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 102524 52 303 0
2023-03-03 102472 31 303 0
2023-03-02 102441 51 303 0
2023-03-01 102390 119 303 0
2023-02-28 102271 164 303 0
2023-02-27 102107 166 303 0
2023-02-24 101941 107 303 0
2023-02-23 101834 78 303 0
2023-02-22 101756 88 303 0
2023-02-21 101668 124 303 0
2023-02-17 101544 36 303 0
2023-02-16 101508 46 303 0
2023-02-15 101462 44 303 0
2023-02-14 101418 53 303 0
2023-02-13 101365 54 303 0
2023-02-10 101311 17 303 0
2023-02-09 101294 24 303 0
2023-02-08 101270 32 303 0
2023-02-07 101238 39 303 0
2023-02-06 101199 60 303 0
2023-02-03 101139 21 303 0
2023-02-02 101118 30 303 0
2023-02-01 101088 24 303 0
2023-01-31 101064 38 303 0
2023-01-30 101026 26 303 0
2023-01-27 101000 12 303 0
2023-01-26 100988 25 303 0
2023-01-25 100963 14 303 0
2023-01-24 100949 22 303 0
2023-01-23 100927 32 303 1
2023-01-20 100895 11 302 0
2023-01-19 100884 21 302 0
2023-01-18 100863 24 302 0
2023-01-17 100839 26 302 1
2023-01-16 100813 46 301 0
2023-01-13 100767 29 301 0
2023-01-12 100738 47 301 0
2023-01-11 100691 33 301 0
2023-01-10 100658 53 301 0
2023-01-09 100605 69 301 0
2023-01-06 100536 51 301 0
2023-01-05 100485 79 301 0
2023-01-04 100406 68 301 0
2023-01-03 100338 99 301 0
2023-01-02 100239 115 301 0
2022-12-30 100124 44 301 0
2022-12-29 100080 82 301 0
2022-12-28 99998 129 301 0
2022-12-27 99869 161 301 1
2022-12-23 99708 56 300 0
2022-12-22 99652 100 300 1
2022-12-21 99552 125 299 2
2022-12-20 99427 136 297 0
2022-12-19 99291 139 297 2
2022-12-16 99152 65 295 0
2022-12-15 99087 63 295 0
2022-12-14 99024 76 295 1
2022-12-13 98948 105 294 0
2022-12-12 98843 115 294 0
2022-12-09 98728 78 294 0
2022-12-08 98650 61 294 0
2022-12-07 98589 102 294 0
2022-12-06 98487 93 294 0
2022-12-05 98394 121 294 0
2022-12-02 98273 47 294 0
2022-12-01 98226 58 294 0
2022-11-30 98168 72 294 0
2022-11-29 98096 95 294 1
2022-11-28 98001 101 293 1
2022-11-25 97900 40 292 0
2022-11-24 97860 62 292 0
2022-11-23 97798 64 292 0
2022-11-22 97734 81 292 0
2022-11-21 97653 110 292 0
2022-11-18 97543 77 292 0
2022-11-17 97466 51 292 1
2022-11-16 97415 75 291 0
2022-11-15 97340 72 291 1
2022-11-14 97268 134 290 0
2022-11-11 97134 55 290 0
2022-11-10 97079 69 290 0
2022-11-09 97010 92 290 0
2022-11-08 96918 96 290 0
2022-11-07 96822 157 290 0
2022-11-04 96665 83 290 0
2022-11-03 96582 100 290 0
2022-11-02 96482 191 290 0
2022-10-31 96291 180 290 0
2022-10-28 96111 112 290 0
2022-10-27 95999 160 290 0
2022-10-26 95839 159 290 0
2022-10-25 95680 282 290 0
2022-10-24 95398 437 290 1
2022-10-21 94961 233 289 0
2022-10-20 94728 314 289 0
2022-10-19 94414 458 289 1
2022-10-18 93956 521 288 2
2022-10-17 93435 576 286 1
2022-10-14 92859 372 285 0
2022-10-13 92487 410 285 0
2022-10-12 92077 564 285 2
2022-10-11 91513 628 283 1
2022-10-10 90885 869 282 0
2022-10-07 90016 505 282 1
2022-10-06 89511 695 281 0
2022-10-05 88816 699 281 0
2022-10-04 88117 768 281 0
2022-09-30 87349 324 281 0
2022-09-29 87025 367 281 1
2022-09-28 86658 425 280 0
2022-09-27 86233 426 280 1
2022-09-26 85807 421 279 0
2022-09-23 85386 186 279 0
2022-09-22 85200 194 279 0
2022-09-21 85006 278 279 0
2022-09-20 84728 282 279 0
2022-09-19 84446 260 279 0
2022-09-16 84186 162 279 1
2022-09-15 84024 128 278 0
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2022-09-13 83720 216 278 2
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2022-09-09 83333 104 276 0
2022-09-08 83229 95 276 0
2022-09-07 83134 103 276 0
2022-09-06 83031 97 276 0
2022-09-05 82934 139 276 0
2022-09-02 82795 62 276 0
2022-09-01 82733 62 276 0
2022-08-31 82671 102 276 1
2022-08-30 82569 115 275 0
2022-08-29 82454 141 275 0
2022-08-26 82313 71 275 0
2022-08-25 82242 76 275 0
2022-08-24 82166 105 275 0
2022-08-23 82061 133 275 0
2022-08-22 81928 180 275 0
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2022-08-18 81636 147 275 0
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2022-08-16 81334 207 275 0
2022-08-12 81127 145 275 0
2022-08-11 80982 91 275 0
2022-08-10 80891 129 275 1
2022-08-09 80762 136 274 0
2022-08-08 80626 222 274 0
2022-08-05 80404 137 274 1
2022-08-04 80267 121 273 1
2022-08-03 80146 157 272 2
2022-08-02 79989 212 270 0
2022-08-01 79777 328 270 1
2022-07-29 79449 193 269 1
2022-07-28 79256 214 268 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: 0:03:56.795072