Germany: LK Wunsiedel i.Fichtelgebirge (Bayern)¶

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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 11:36:29 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Wunsiedel i.Fichtelgebirge", weeks=5);
2023-03-07T11:36:40.706351 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 148.3 LK Wunsiedel i.Fichtelgebirge, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 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 Wunsiedel i.Fichtelgebirge cases daily growth factor Germany-LK Wunsiedel i.Fichtelgebirge cases daily growth factor (rolling mean) Germany-LK Wunsiedel i.Fichtelgebirge estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Wunsiedel i.Fichtelgebirge deaths daily growth factor Germany-LK Wunsiedel i.Fichtelgebirge deaths daily growth factor (rolling mean) Germany-LK Wunsiedel i.Fichtelgebirge 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 Wunsiedel i.Fichtelgebirge doubling time cases (rolling mean) 0.00 14.30 28.59 42.89 daily change Germany-LK Wunsiedel i.Fichtelgebirge new cases (rolling 7d mean) Germany-LK Wunsiedel i.Fichtelgebirge new cases 0.000 0.357 0.715 daily change Germany-LK Wunsiedel i.Fichtelgebirge new deaths (rolling 7d mean) Germany-LK Wunsiedel i.Fichtelgebirge new deaths 0.000 0.333 0.665 0.998
In [4]:
overview(country="Germany", subregion="LK Wunsiedel i.Fichtelgebirge");
2023-03-07T11:36:56.955300 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) 148.3 LK Wunsiedel i.Fichtelgebirge, 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 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Wunsiedel i.Fichtelgebirge cases daily growth factor Germany-LK Wunsiedel i.Fichtelgebirge cases daily growth factor (rolling mean) Germany-LK Wunsiedel i.Fichtelgebirge 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 4 4 R & growth factor (based on deaths) Germany-LK Wunsiedel i.Fichtelgebirge deaths daily growth factor Germany-LK Wunsiedel i.Fichtelgebirge deaths daily growth factor (rolling mean) Germany-LK Wunsiedel i.Fichtelgebirge 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 Wunsiedel i.Fichtelgebirge doubling time cases (rolling mean) Germany-LK Wunsiedel i.Fichtelgebirge doubling time deaths (rolling mean) 0.0 143.0 285.9 428.9 571.9 daily change Germany-LK Wunsiedel i.Fichtelgebirge new cases (rolling 7d mean) Germany-LK Wunsiedel i.Fichtelgebirge new cases 0.000 1.430 2.859 4.289 5.719 daily change Germany-LK Wunsiedel i.Fichtelgebirge new deaths (rolling 7d mean) Germany-LK Wunsiedel i.Fichtelgebirge new deaths 0.0 65.3 130.5 195.8 261.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Wunsiedel i.Fichtelgebirge", dates="2020-03-15:");
2023-03-07T11:39:01.949348 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 Wunsiedel i.Fichtelgebirge LK Wunsiedel i.Fichtelgebirge 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 Wunsiedel i.Fichtelgebirge Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Wunsiedel i.Fichtelgebirge")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Wunsiedel i.Fichtelgebirge")
print(f'Population of country="Germany", subregion="LK Wunsiedel i.Fichtelgebirge": {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 Wunsiedel i.Fichtelgebirge": 71482 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 38266 15 325 0
2023-03-03 38251 11 325 0
2023-03-02 38240 5 325 0
2023-03-01 38235 32 325 0
2023-02-28 38203 43 325 0
2023-02-27 38160 37 325 1
2023-02-24 38123 24 324 0
2023-02-23 38099 29 324 0
2023-02-22 38070 25 324 0
2023-02-21 38045 38 324 0
2023-02-20 38007 28 324 0
2023-02-17 37979 13 324 0
2023-02-16 37966 26 324 0
2023-02-15 37940 19 324 0
2023-02-14 37921 28 324 0
2023-02-13 37893 26 324 0
2023-02-10 37867 15 324 1
2023-02-09 37852 7 323 0
2023-02-08 37845 28 323 0
2023-02-07 37817 25 323 1
2023-02-06 37792 38 322 0
2023-02-03 37754 16 322 0
2023-02-02 37738 9 322 0
2023-02-01 37729 15 322 0
2023-01-31 37714 13 322 0
2023-01-30 37701 24 322 0
2023-01-27 37677 9 322 0
2023-01-26 37668 7 322 0
2023-01-25 37661 8 322 0
2023-01-24 37653 24 322 0
2023-01-23 37629 8 322 0
2023-01-20 37621 3 322 0
2023-01-19 37618 3 322 0
2023-01-18 37615 5 322 0
2023-01-17 37610 12 322 0
2023-01-16 37598 7 322 0
2023-01-13 37591 5 322 0
2023-01-12 37586 2 322 0
2023-01-11 37584 18 322 0
2023-01-10 37566 17 322 0
2023-01-09 37549 32 322 1
2023-01-05 37517 17 321 0
2023-01-04 37500 15 321 2
2023-01-03 37485 15 319 0
2023-01-02 37470 20 319 1
2022-12-30 37450 12 318 1
2022-12-29 37438 27 317 0
2022-12-28 37411 30 317 1
2022-12-27 37381 53 316 1
2022-12-23 37328 22 315 1
2022-12-22 37306 21 314 0
2022-12-21 37285 30 314 1
2022-12-20 37255 34 313 0
2022-12-19 37221 32 313 0
2022-12-16 37189 10 313 0
2022-12-15 37179 8 313 0
2022-12-14 37171 20 313 0
2022-12-13 37151 40 313 0
2022-12-12 37111 21 313 0
2022-12-10 37090 14 313 1
2022-12-09 37076 18 312 0
2022-12-08 37058 21 312 0
2022-12-07 37037 26 312 0
2022-12-06 37011 26 312 1
2022-12-05 36985 27 311 0
2022-12-02 36958 14 311 0
2022-12-01 36944 19 311 0
2022-11-30 36925 14 311 0
2022-11-29 36911 18 311 0
2022-11-28 36893 23 311 0
2022-11-25 36870 18 311 1
2022-11-24 36852 25 310 0
2022-11-23 36827 13 310 2
2022-11-22 36814 25 308 0
2022-11-21 36789 16 308 0
2022-11-19 36773 19 308 0
2022-11-18 36754 12 308 0
2022-11-17 36742 17 308 0
2022-11-16 36725 15 308 0
2022-11-15 36710 23 308 0
2022-11-14 36687 15 308 0
2022-11-13 36672 4 308 0
2022-11-12 36668 20 308 1
2022-11-11 36648 17 307 0
2022-11-10 36631 31 307 0
2022-11-09 36600 32 307 0
2022-11-08 36568 54 307 0
2022-11-07 36514 38 307 1
2022-11-06 36476 3 306 0
2022-11-05 36473 30 306 1
2022-11-04 36443 32 305 0
2022-11-03 36411 59 305 2
2022-11-02 36352 14 303 0
2022-11-01 36338 47 303 0
2022-10-31 36291 26 303 0
2022-10-30 36265 5 303 0
2022-10-29 36260 31 303 0
2022-10-28 36229 41 303 0
2022-10-27 36188 37 303 0
2022-10-26 36151 46 303 1
2022-10-25 36105 81 302 0
2022-10-24 36024 24 302 0
2022-10-23 36000 3 302 1
2022-10-22 35997 37 301 0
2022-10-21 35960 66 301 0
2022-10-20 35894 51 301 0
2022-10-19 35843 68 301 0
2022-10-18 35775 105 301 0
2022-10-17 35670 66 301 1
2022-10-16 35604 4 300 0
2022-10-15 35600 83 300 1
2022-10-14 35517 67 299 0
2022-10-13 35450 94 299 0
2022-10-12 35356 77 299 0
2022-10-11 35279 201 299 0
2022-10-10 35078 34 299 0
2022-10-09 35044 5 299 0
2022-10-08 35039 72 299 0
2022-10-07 34967 107 299 1
2022-10-06 34860 134 298 1
2022-10-05 34726 124 297 1
2022-10-04 34602 41 296 0
2022-10-03 34561 30 296 1
2022-10-02 34531 9 295 0
2022-10-01 34522 59 295 1
2022-09-30 34463 69 294 2
2022-09-29 34394 68 292 0
2022-09-28 34326 82 292 1
2022-09-27 34244 108 291 2
2022-09-26 34136 24 289 0
2022-09-25 34112 5 289 0
2022-09-24 34107 45 289 0
2022-09-23 34062 45 289 0
2022-09-22 34017 39 289 0
2022-09-21 33978 50 289 0
2022-09-20 33928 57 289 0
2022-09-19 33871 12 289 0
2022-09-18 33859 3 289 0
2022-09-17 33856 27 289 0
2022-09-16 33829 29 289 0
2022-09-15 33800 22 289 0
2022-09-14 33778 19 289 0
2022-09-13 33759 44 289 0
2022-09-12 33715 23 289 0
2022-09-11 33692 2 289 0
2022-09-10 33690 19 289 0
2022-09-09 33671 20 289 1
2022-09-08 33651 29 288 0
2022-09-07 33622 24 288 0
2022-09-06 33598 40 288 0
2022-09-05 33558 12 288 0
2022-09-04 33546 2 288 0
2022-09-03 33544 20 288 0
2022-09-02 33524 26 288 0
2022-09-01 33498 20 288 0
2022-08-31 33478 17 288 0
2022-08-30 33461 30 288 1
2022-08-29 33431 12 287 0
2022-08-28 33419 1 287 0
2022-08-27 33418 22 287 0
2022-08-26 33396 19 287 0
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2022-08-23 33336 35 287 0
2022-08-22 33301 20 287 0
2022-08-21 33281 7 287 1
2022-08-20 33274 18 286 0
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2022-08-03 32766 54 284 0
2022-08-02 32712 75 284 0
2022-08-01 32637 62 284 1
2022-07-31 32575 6 283 0
2022-07-30 32569 57 283 0
2022-07-29 32512 76 283 0
2022-07-28 32436 98 283 1
2022-07-27 32338 102 282 0
2022-07-26 32236 298 282 0
2022-07-25 31938 106 282 0
2022-07-24 31832 15 282 0
2022-07-23 31817 187 282 0
2022-07-22 31630 195 282 0
2022-07-21 31435 240 282 1
2022-07-20 31195 342 281 0
2022-07-19 30853 585 281 0
2022-07-18 30268 201 281 1
2022-07-17 30067 40 280 2
2022-07-16 30027 389 278 0
2022-07-15 29638 417 278 2
2022-07-14 29221 306 276 0
2022-07-13 28915 195 276 0
2022-07-12 28720 188 276 2
2022-07-11 28532 199 274 1
2022-07-08 28333 84 273 1
2022-07-07 28249 92 272 0
2022-07-06 28157 73 272 0
2022-07-05 28084 103 272 0
2022-07-04 27981 36 272 0
2022-07-03 27945 38 272 0
2022-07-02 27907 49 272 0
2022-07-01 27858 45 272 1
2022-06-30 27813 52 271 0
2022-06-29 27761 79 271 1
2022-06-28 27682 85 270 0
2022-06-27 27597 35 270 0
2022-06-26 27562 11 270 0
2022-06-25 27551 59 270 1
2022-06-24 27492 39 269 0
2022-06-23 27453 53 269 0
2022-06-22 27400 58 269 0
2022-06-21 27342 91 269 0
2022-06-20 27251 15 269 0
2022-06-19 27236 39 269 0
2022-06-18 27197 49 269 0
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2022-06-16 27131 33 269 0
2022-06-15 27098 56 269 1
2022-06-14 27042 55 268 0
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2022-06-12 26968 23 268 0
2022-06-11 26945 67 268 0
2022-06-10 26878 17 268 1
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2022-06-08 26794 37 267 0
2022-06-07 26757 22 267 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: 0:02:45.089102