Germany: LK Bad Kissingen (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 10:28:37 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Bad Kissingen", weeks=5);
2023-03-07T10:28:45.877690 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 125 125 150 150 175 175 200 200 225 225 7-day incidence rate (per 100K people) 138.2 LK Bad Kissingen, 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 0.2 0.4 0.6 0.8 daily change normalised per 100K 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 cases) Germany-LK Bad Kissingen cases daily growth factor Germany-LK Bad Kissingen cases daily growth factor (rolling mean) Germany-LK Bad Kissingen 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 Bad Kissingen deaths daily growth factor Germany-LK Bad Kissingen deaths daily growth factor (rolling mean) Germany-LK Bad Kissingen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 2000 cases doubling time [days] Germany-LK Bad Kissingen doubling time cases (rolling mean) 0.00 20.69 41.38 62.07 daily change Germany-LK Bad Kissingen new cases (rolling 7d mean) Germany-LK Bad Kissingen new cases 0.000 0.207 0.414 0.621 0.828 daily change Germany-LK Bad Kissingen new deaths (rolling 7d mean) Germany-LK Bad Kissingen new deaths 0.000 0.221 0.443 0.664 0.886
In [4]:
overview(country="Germany", subregion="LK Bad Kissingen");
2023-03-07T10:29:17.608829 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) 138.2 LK Bad Kissingen, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 2.5 5.0 7.5 10.0 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Germany-LK Bad Kissingen cases daily growth factor Germany-LK Bad Kissingen cases daily growth factor (rolling mean) Germany-LK Bad Kissingen 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 Bad Kissingen deaths daily growth factor Germany-LK Bad Kissingen deaths daily growth factor (rolling mean) Germany-LK Bad Kissingen 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 cases doubling time [days] Germany-LK Bad Kissingen doubling time cases (rolling mean) Germany-LK Bad Kissingen doubling time deaths (rolling mean) 0.0 206.9 413.8 620.7 daily change Germany-LK Bad Kissingen new cases (rolling 7d mean) Germany-LK Bad Kissingen new cases 0.00 2.59 5.17 7.76 10.35 daily change Germany-LK Bad Kissingen new deaths (rolling 7d mean) Germany-LK Bad Kissingen new deaths 0.0 54.2 108.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Bad Kissingen", dates="2020-03-15:");
2023-03-07T10:30:23.021828 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 Bad Kissingen LK Bad Kissingen 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Bad Kissingen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Bad Kissingen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Bad Kissingen")
print(f'Population of country="Germany", subregion="LK Bad Kissingen": {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 Bad Kissingen": 103454 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 56589 18 244 0
2023-03-03 56571 26 244 0
2023-03-02 56545 19 244 1
2023-03-01 56526 42 243 0
2023-02-28 56484 38 243 0
2023-02-27 56446 65 243 0
2023-02-24 56381 23 243 0
2023-02-23 56358 38 243 0
2023-02-22 56320 25 243 1
2023-02-21 56295 42 242 0
2023-02-20 56253 42 242 0
2023-02-17 56211 24 242 0
2023-02-16 56187 34 242 0
2023-02-15 56153 37 242 0
2023-02-14 56116 55 242 0
2023-02-13 56061 46 242 0
2023-02-10 56015 46 242 0
2023-02-09 55969 27 242 0
2023-02-08 55942 27 242 1
2023-02-07 55915 49 241 0
2023-02-06 55866 66 241 0
2023-02-03 55800 36 241 0
2023-02-02 55764 39 241 0
2023-02-01 55725 45 241 1
2023-01-31 55680 29 240 0
2023-01-30 55651 31 240 0
2023-01-27 55620 29 240 0
2023-01-26 55591 20 240 1
2023-01-25 55571 24 239 0
2023-01-24 55547 33 239 1
2023-01-23 55514 27 238 0
2023-01-20 55487 19 238 0
2023-01-19 55468 18 238 0
2023-01-18 55450 11 238 1
2023-01-17 55439 22 237 0
2023-01-16 55417 25 237 0
2023-01-13 55392 16 237 0
2023-01-12 55376 23 237 1
2023-01-11 55353 15 236 0
2023-01-10 55338 34 236 0
2023-01-09 55304 29 236 1
2023-01-05 55275 23 235 1
2023-01-04 55252 20 234 0
2023-01-03 55232 37 234 0
2023-01-02 55195 31 234 1
2022-12-30 55164 18 233 1
2022-12-29 55146 32 232 0
2022-12-28 55114 38 232 0
2022-12-27 55076 49 232 0
2022-12-23 55027 17 232 0
2022-12-22 55010 26 232 0
2022-12-21 54984 33 232 0
2022-12-20 54951 31 232 0
2022-12-19 54920 42 232 2
2022-12-16 54878 17 230 0
2022-12-15 54861 23 230 0
2022-12-14 54838 35 230 1
2022-12-13 54803 26 229 0
2022-12-12 54777 27 229 0
2022-12-09 54750 14 229 1
2022-12-08 54736 32 228 0
2022-12-07 54704 33 228 0
2022-12-06 54671 26 228 0
2022-12-05 54645 30 228 0
2022-12-02 54615 19 228 0
2022-12-01 54596 34 228 0
2022-11-30 54562 33 228 0
2022-11-29 54529 29 228 1
2022-11-28 54500 36 227 0
2022-11-25 54464 19 227 0
2022-11-24 54445 25 227 1
2022-11-23 54420 38 226 0
2022-11-22 54382 40 226 0
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2022-11-18 54297 26 226 1
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2022-10-31 53537 121 225 1
2022-10-28 53416 63 224 0
2022-10-27 53353 96 224 1
2022-10-26 53257 78 223 1
2022-10-25 53179 333 222 0
2022-10-24 52846 199 222 0
2022-10-21 52647 103 222 0
2022-10-20 52544 130 222 1
2022-10-19 52414 142 221 0
2022-10-18 52272 186 221 1
2022-10-17 52086 285 220 0
2022-10-14 51801 165 220 0
2022-10-13 51636 187 220 0
2022-10-12 51449 264 220 1
2022-10-11 51185 239 219 0
2022-10-10 50946 416 219 0
2022-10-07 50530 239 219 0
2022-10-06 50291 359 219 0
2022-10-05 49932 223 219 1
2022-10-04 49709 432 218 0
2022-09-30 49277 104 218 0
2022-09-29 49173 202 218 0
2022-09-28 48971 121 218 0
2022-09-27 48850 169 218 0
2022-09-26 48681 133 218 0
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2022-09-22 48433 81 218 0
2022-09-21 48352 96 218 0
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2022-09-09 47722 23 218 1
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2021-05-06 3402 15 102 2
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2021-04-19 3010 20 95 0
2021-04-18 2990 8 95 0
2021-04-17 2982 16 95 0
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2021-04-15 2954 27 95 0
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2021-04-05 2752 4 93 0
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2021-03-30 2675 24 92 0
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2021-02-01 2081 4 81 0
2021-01-31 2077 4 81 0
2021-01-30 2073 11 81 1
2021-01-29 2062 22 80 0
2021-01-28 2040 23 80 0
2021-01-27 2017 9 80 0
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2021-01-22 1972 22 80 0
2021-01-21 1950 16 80 1
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2021-01-18 1899 2 79 0
2021-01-17 1897 2 79 0
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2021-01-15 1874 29 79 1
2021-01-14 1845 12 78 0
2021-01-13 1833 23 78 2
2021-01-12 1810 18 76 0
2021-01-10 1792 2 76 1
2021-01-09 1790 35 75 2
2021-01-08 1755 15 73 0
2021-01-07 1740 21 73 2
2021-01-06 1719 35 71 0
2021-01-05 1684 23 71 0
2021-01-04 1661 13 71 0
2021-01-03 1648 3 71 0
2021-01-02 1645 8 71 2
2021-01-01 1637 16 69 1
2020-12-31 1621 27 68 1
2020-12-30 1594 55 67 3
2020-12-29 1539 16 64 2
2020-12-28 1523 17 62 0
2020-12-27 1506 4 62 0
2020-12-26 1502 26 62 2
2020-12-25 1476 67 60 12
2020-12-24 1409 34 48 3
2020-12-23 1375 24 45 2
2020-12-22 1351 23 43 2
2020-12-21 1328 15 41 1
2020-12-20 1313 24 40 2
2020-12-19 1289 37 38 1
2020-12-18 1252 15 37 1
2020-12-17 1237 30 36 2
2020-12-16 1207 41 34 0
2020-12-15 1166 27 34 0
2020-12-14 1139 15 34 0
2020-12-13 1124 2 34 1
2020-12-12 1122 38 33 1
2020-12-11 1084 32 32 0
2020-12-10 1052 30 32 0
2020-12-09 1022 44 32 2
2020-12-08 978 36 30 5
2020-12-07 942 20 25 2
2020-12-06 922 7 23 1
2020-12-05 915 28 22 0
2020-12-04 887 23 22 0
2020-12-03 864 18 22 0
2020-12-02 846 31 22 0
2020-12-01 815 13 22 1
2020-11-30 802 6 21 0
2020-11-29 796 8 21 0
2020-11-28 788 8 21 1
2020-11-27 780 28 20 0
2020-11-26 752 16 20 0
2020-11-25 736 20 20 0
2020-11-24 716 26 20 0
2020-11-23 690 4 20 0
2020-11-21 686 25 20 1
2020-11-20 661 22 19 0
2020-11-19 639 12 19 0
2020-11-18 627 22 19 0
2020-11-17 605 22 19 0
2020-11-16 583 7 19 0
2020-11-15 576 18 19 0
2020-11-13 558 19 19 1
2020-11-12 539 10 18 0
2020-11-11 529 22 18 0
2020-11-10 507 14 18 0
2020-11-09 493 10 18 0
2020-11-08 483 5 18 0
2020-11-07 478 7 18 0
2020-11-06 471 9 18 0
2020-11-05 462 17 18 0
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2020-11-03 436 9 18 0
2020-11-02 427 4 18 0
2020-11-01 423 12 18 0
2020-10-31 411 6 18 0
2020-10-30 405 8 18 0
2020-10-29 397 11 18 0
2020-10-28 386 11 18 0
2020-10-27 375 9 18 0
2020-10-26 366 6 18 0
2020-10-25 360 3 18 0
2020-10-24 357 7 18 0
2020-10-23 350 17 18 0
2020-10-22 333 6 18 0
2020-10-21 327 10 18 0
2020-10-20 317 2 18 0
2020-10-19 315 4 18 0
2020-10-16 311 1 18 0
2020-10-15 310 3 18 0
2020-10-14 307 1 18 0
2020-10-13 306 2 18 0
2020-10-12 304 7 18 0
2020-10-09 297 2 18 0
2020-10-05 295 1 18 0
2020-10-02 294 1 18 0
2020-09-30 293 1 18 0
2020-09-29 292 2 18 0
2020-09-28 290 1 18 0
2020-09-23 289 1 18 0
2020-09-22 288 2 18 0
2020-09-21 286 1 18 0
2020-09-14 285 1 18 0
2020-09-07 284 3 18 0
2020-09-04 281 1 18 0
2020-09-03 280 1 18 0
2020-08-31 279 1 18 0
2020-08-25 278 3 18 0
2020-08-24 275 2 18 0
2020-08-19 273 1 18 0
2020-08-17 272 2 18 0
2020-08-14 270 2 18 0
2020-08-12 268 2 18 0
2020-08-11 266 2 18 0
2020-08-04 264 2 18 0
2020-07-21 262 3 18 0
2020-07-17 259 3 18 0
2020-06-12 256 1 18 0
2020-06-03 255 1 18 0
2020-05-30 254 1 18 0
2020-05-29 253 2 18 0
2020-05-21 251 1 18 0
2020-05-20 250 2 18 0
2020-05-16 248 3 18 0
2020-05-15 245 1 18 0
2020-05-14 244 3 18 0
2020-05-13 241 2 18 0
2020-05-12 239 3 18 0
2020-05-11 236 2 18 0
2020-05-09 234 2 18 0
2020-05-01 232 1 18 0
2020-04-30 231 5 18 0
2020-04-28 226 1 18 0
2020-04-27 225 2 18 0
2020-04-25 223 3 18 0
2020-04-24 220 5 18 0
2020-04-23 215 1 18 0
2020-04-22 214 1 18 1
2020-04-21 213 1 17 0
2020-04-20 212 1 17 0
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2020-04-18 210 2 17 0
2020-04-17 208 8 17 4
2020-04-16 200 4 13 0
2020-04-15 196 6 13 1
2020-04-14 190 14 12 0
2020-04-13 176 8 12 0
2020-04-12 168 1 12 1
2020-04-11 167 12 11 2
2020-04-09 155 3 9 1
2020-04-08 152 6 8 1
2020-04-07 146 16 7 0
2020-04-06 130 2 7 0
2020-04-05 128 1 7 0
2020-04-04 127 3 7 1
2020-04-03 124 9 6 1
2020-04-02 115 8 5 0
2020-04-01 107 11 5 1
2020-03-31 96 23 4 3
2020-03-30 73 5 1 0
2020-03-29 68 4 1 0
2020-03-28 64 9 1 0
2020-03-27 55 19 1 1
2020-03-26 36 3 0 0
2020-03-25 33 7 0 0
2020-03-24 26 4 0 0
2020-03-23 22 2 0 0
2020-03-22 20 4 0 0
2020-03-21 16 2 0 0
2020-03-20 14 5 0 0
2020-03-18 9 2 0 0
2020-03-17 7 4 0 0
2020-03-11 3 2 0 0

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: 0:01:58.066986