Germany: LK Stade (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 13:45:08 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Stade", weeks=5);
2023-03-07T13:45:19.974830 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) 34.9 LK Stade, 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 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Stade cases daily growth factor Germany-LK Stade cases daily growth factor (rolling mean) Germany-LK Stade 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Stade deaths daily growth factor Germany-LK Stade deaths daily growth factor (rolling mean) Germany-LK Stade 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 Stade doubling time cases (rolling mean) 0.00 20.65 41.30 daily change Germany-LK Stade new cases (rolling 7d mean) Germany-LK Stade new cases 0.000 0.206 0.413 0.619 0.826 daily change Germany-LK Stade new deaths (rolling 7d mean) Germany-LK Stade new deaths 0.000 0.246 0.492 0.738 0.984
In [4]:
overview(country="Germany", subregion="LK Stade");
2023-03-07T13:45:36.936480 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) 34.9 LK Stade, 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 2 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Stade cases daily growth factor Germany-LK Stade cases daily growth factor (rolling mean) Germany-LK Stade 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Stade deaths daily growth factor Germany-LK Stade deaths daily growth factor (rolling mean) Germany-LK Stade 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 Stade doubling time cases (rolling mean) Germany-LK Stade doubling time deaths (rolling mean) 0 206 413 619 826 daily change Germany-LK Stade new cases (rolling 7d mean) Germany-LK Stade new cases 0.00 4.13 8.26 daily change Germany-LK Stade new deaths (rolling 7d mean) Germany-LK Stade new deaths 0.0 94.4 188.9 283.3 377.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Stade", dates="2020-03-15:");
2023-03-07T13:48:03.150216 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 Stade LK Stade 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 Stade Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Stade")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Stade")
print(f'Population of country="Germany", subregion="LK Stade": {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 Stade": 206496 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 99556 1 343 0
2023-03-05 99555 1 343 0
2023-03-04 99554 3 343 0
2023-03-03 99551 12 343 0
2023-03-02 99539 13 343 0
2023-03-01 99526 21 343 0
2023-02-28 99505 21 343 0
2023-02-27 99484 9 343 0
2023-02-26 99475 6 343 0
2023-02-25 99469 20 343 0
2023-02-24 99449 30 343 0
2023-02-23 99419 53 343 0
2023-02-22 99366 28 343 0
2023-02-21 99338 51 343 0
2023-02-20 99287 59 343 0
2023-02-19 99228 12 343 0
2023-02-18 99216 14 343 0
2023-02-17 99202 38 343 0
2023-02-16 99164 22 343 0
2023-02-15 99142 42 343 0
2023-02-14 99100 35 343 0
2023-02-13 99065 42 343 0
2023-02-12 99023 6 343 0
2023-02-11 99017 13 343 0
2023-02-10 99004 26 343 0
2023-02-09 98978 23 343 1
2023-02-08 98955 25 342 0
2023-02-07 98930 33 342 0
2023-02-06 98897 41 342 0
2023-02-05 98856 8 342 0
2023-02-04 98848 4 342 0
2023-02-03 98844 39 342 0
2023-02-02 98805 25 342 0
2023-02-01 98780 28 342 0
2023-01-31 98752 31 342 0
2023-01-30 98721 42 342 0
2023-01-29 98679 3 342 0
2023-01-28 98676 20 342 0
2023-01-27 98656 26 342 0
2023-01-26 98630 26 342 0
2023-01-25 98604 42 342 0
2023-01-24 98562 52 342 1
2023-01-23 98510 41 341 0
2023-01-22 98469 7 341 0
2023-01-21 98462 16 341 0
2023-01-20 98446 19 341 0
2023-01-19 98427 31 341 0
2023-01-18 98396 27 341 0
2023-01-17 98369 50 341 0
2023-01-16 98319 41 341 0
2023-01-15 98278 6 341 0
2023-01-14 98272 34 341 0
2023-01-13 98238 30 341 1
2023-01-12 98208 44 340 1
2023-01-11 98164 32 339 0
2023-01-10 98132 62 339 1
2023-01-09 98070 68 338 0
2023-01-08 98002 8 338 0
2023-01-07 97994 12 338 0
2023-01-06 97982 59 338 2
2023-01-05 97923 69 336 2
2023-01-04 97854 64 334 1
2023-01-03 97790 138 333 1
2023-01-02 97652 115 332 0
2023-01-01 97537 30 332 0
2022-12-31 97507 16 332 0
2022-12-30 97491 105 332 1
2022-12-29 97386 119 331 1
2022-12-28 97267 187 330 0
2022-12-27 97080 250 330 2
2022-12-26 96830 54 328 1
2022-12-25 96776 39 327 1
2022-12-24 96737 70 326 1
2022-12-23 96667 180 325 2
2022-12-22 96487 244 323 0
2022-12-21 96243 237 323 6
2022-12-20 96006 420 317 1
2022-12-19 95586 156 316 2
2022-12-18 95430 54 314 0
2022-12-17 95376 111 314 1
2022-12-16 95265 203 313 2
2022-12-15 95062 241 311 1
2022-12-14 94821 243 310 1
2022-12-13 94578 289 309 1
2022-12-12 94289 201 308 4
2022-12-11 94088 45 304 1
2022-12-10 94043 88 303 1
2022-12-09 93955 204 302 0
2022-12-08 93751 218 302 1
2022-12-07 93533 181 301 0
2022-12-06 93352 333 301 0
2022-12-05 93019 232 301 0
2022-12-04 92787 58 301 2
2022-12-03 92729 101 299 2
2022-12-02 92628 168 297 0
2022-12-01 92460 214 297 1
2022-11-30 92246 247 296 2
2022-11-29 91999 355 294 1
2022-11-28 91644 196 293 3
2022-11-27 91448 24 290 0
2022-11-26 91424 115 290 0
2022-11-25 91309 171 290 1
2022-11-24 91138 149 289 0
2022-11-23 90989 209 289 1
2022-11-22 90780 216 288 2
2022-11-21 90564 156 286 0
2022-11-20 90408 31 286 2
2022-11-19 90377 65 284 0
2022-11-18 90312 131 284 0
2022-11-17 90181 149 284 0
2022-11-16 90032 132 284 0
2022-11-15 89900 153 284 0
2022-11-14 89747 161 284 1
2022-11-13 89586 24 283 1
2022-11-12 89562 57 282 1
2022-11-11 89505 116 281 1
2022-11-10 89389 155 280 0
2022-11-09 89234 139 280 0
2022-11-08 89095 154 280 1
2022-11-07 88941 171 279 1
2022-11-06 88770 30 278 0
2022-11-05 88740 77 278 0
2022-11-04 88663 125 278 0
2022-11-03 88538 193 278 0
2022-11-02 88345 237 278 0
2022-11-01 88108 152 278 1
2022-10-31 87956 49 277 0
2022-10-30 87907 54 277 0
2022-10-29 87853 51 277 1
2022-10-28 87802 183 276 1
2022-10-27 87619 156 275 1
2022-10-26 87463 162 274 1
2022-10-25 87301 256 273 1
2022-10-24 87045 266 272 0
2022-10-23 86779 62 272 0
2022-10-22 86717 84 272 0
2022-10-21 86633 224 272 1
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2022-10-19 86184 242 270 2
2022-10-18 85942 378 268 1
2022-10-17 85564 374 267 0
2022-10-16 85190 70 267 0
2022-10-15 85120 60 267 1
2022-10-14 85060 293 266 0
2022-10-13 84767 362 266 1
2022-10-12 84405 279 265 1
2022-10-11 84126 458 264 2
2022-10-10 83668 257 262 2
2022-10-09 83411 127 260 0
2022-10-08 83284 138 260 1
2022-10-07 83146 298 259 0
2022-10-06 82848 392 259 0
2022-10-05 82456 471 259 0
2022-10-04 81985 335 259 2
2022-10-03 81650 113 257 1
2022-10-02 81537 70 256 1
2022-10-01 81467 122 255 0
2022-09-30 81345 212 255 1
2022-09-29 81133 263 254 0
2022-09-28 80870 212 254 0
2022-09-27 80658 291 254 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: 0:04:13.777545