Germany: LK Bad Dürkheim (Rheinland-Pfalz)¶

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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 14:56:37 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Bad Dürkheim", weeks=5);
2023-03-07T14:56:51.016920 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 69.1 LK Bad Dürkheim, 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 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.00 0.01 0.01 0.02 0.02 0.03 0.03 daily change Germany-LK Bad Dürkheim new deaths (rolling 7d mean) Germany-LK Bad Dürkheim new deaths 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 Bad Dürkheim cases daily growth factor Germany-LK Bad Dürkheim cases daily growth factor (rolling mean) Germany-LK Bad Dürkheim 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 Bad Dürkheim deaths daily growth factor Germany-LK Bad Dürkheim deaths daily growth factor (rolling mean) Germany-LK Bad Dürkheim 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 Bad Dürkheim doubling time cases (rolling mean) 0.00 13.32 26.64 39.96 53.28 daily change Germany-LK Bad Dürkheim new cases (rolling 7d mean) Germany-LK Bad Dürkheim new cases 0.000 0.262 0.525 0.787
In [4]:
overview(country="Germany", subregion="LK Bad Dürkheim");
2023-03-07T14:57:04.335054 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) 69.1 LK Bad Dürkheim, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Bad Dürkheim cases daily growth factor Germany-LK Bad Dürkheim cases daily growth factor (rolling mean) Germany-LK Bad Dürkheim 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 Bad Dürkheim deaths daily growth factor Germany-LK Bad Dürkheim deaths daily growth factor (rolling mean) Germany-LK Bad Dürkheim estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 cases doubling time [days] Germany-LK Bad Dürkheim doubling time cases (rolling mean) Germany-LK Bad Dürkheim doubling time deaths (rolling mean) 0.0 266.4 532.8 daily change Germany-LK Bad Dürkheim new cases (rolling 7d mean) Germany-LK Bad Dürkheim new cases 0.000 1.332 2.664 3.996 5.328 daily change Germany-LK Bad Dürkheim new deaths (rolling 7d mean) Germany-LK Bad Dürkheim new deaths 0.0 103.8 207.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Bad Dürkheim", dates="2020-03-15:");
2023-03-07T14:59:43.897082 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 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 Dürkheim LK Bad Dürkheim 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 Bad Dürkheim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Bad Dürkheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Bad Dürkheim")
print(f'Population of country="Germany", subregion="LK Bad Dürkheim": {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 Dürkheim": 133206 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 52791 10 266 0
2023-03-03 52781 11 266 0
2023-03-02 52770 21 266 0
2023-03-01 52749 25 266 0
2023-02-28 52724 25 266 0
2023-02-27 52699 58 266 0
2023-02-24 52641 26 266 0
2023-02-23 52615 37 266 0
2023-02-22 52578 43 266 0
2023-02-21 52535 16 266 0
2023-02-20 52519 38 266 0
2023-02-16 52481 30 266 0
2023-02-15 52451 44 266 0
2023-02-14 52407 23 266 0
2023-02-13 52384 30 266 0
2023-02-10 52354 25 266 0
2023-02-09 52329 33 266 0
2023-02-08 52296 33 266 0
2023-02-07 52263 33 266 0
2023-02-06 52230 60 266 0
2023-02-03 52170 13 266 0
2023-02-02 52157 25 266 0
2023-02-01 52132 16 266 0
2023-01-31 52116 30 266 0
2023-01-30 52086 43 266 0
2023-01-27 52043 12 266 0
2023-01-26 52031 21 266 1
2023-01-25 52010 37 265 0
2023-01-24 51973 22 265 0
2023-01-23 51951 31 265 0
2023-01-22 51920 1 265 0
2023-01-20 51919 10 265 0
2023-01-19 51909 25 265 0
2023-01-18 51884 24 265 1
2023-01-17 51860 21 264 0
2023-01-16 51839 30 264 0
2023-01-13 51809 12 264 0
2023-01-12 51797 47 264 0
2023-01-11 51750 43 264 0
2023-01-10 51707 45 264 0
2023-01-09 51662 63 264 0
2023-01-06 51599 71 264 0
2023-01-05 51528 118 264 2
2023-01-04 51410 105 262 3
2023-01-03 51305 125 259 0
2023-01-02 51180 78 259 0
2022-12-31 51102 1 259 0
2022-12-30 51101 1 259 0
2022-12-23 51100 19 259 2
2022-12-22 51081 55 257 0
2022-12-21 51026 75 257 0
2022-12-20 50951 53 257 0
2022-12-19 50898 63 257 2
2022-12-16 50835 41 255 1
2022-12-15 50794 84 254 2
2022-12-14 50710 49 252 2
2022-12-13 50661 53 250 0
2022-12-12 50608 76 250 0
2022-12-11 50532 2 250 0
2022-12-10 50530 1 250 0
2022-12-09 50529 37 250 1
2022-12-08 50492 3 249 0
2022-12-07 50489 30 249 0
2022-12-06 50459 47 249 0
2022-12-05 50412 57 249 0
2022-12-02 50355 31 249 0
2022-12-01 50324 43 249 0
2022-11-30 50281 33 249 0
2022-11-29 50248 28 249 0
2022-11-28 50220 74 249 0
2022-11-25 50146 31 249 1
2022-11-24 50115 25 248 0
2022-11-23 50090 87 248 0
2022-11-22 50003 49 248 0
2022-11-21 49954 85 248 0
2022-11-18 49869 24 248 0
2022-11-17 49845 60 248 0
2022-11-16 49785 68 248 0
2022-11-15 49717 63 248 0
2022-11-14 49654 81 248 2
2022-11-11 49573 52 246 0
2022-11-10 49521 23 246 0
2022-11-09 49498 56 246 0
2022-11-08 49442 77 246 0
2022-11-07 49365 66 246 0
2022-11-04 49299 62 246 0
2022-11-03 49237 132 246 1
2022-11-02 49105 162 245 0
2022-10-28 48943 48 245 0
2022-10-27 48895 71 245 0
2022-10-26 48824 99 245 0
2022-10-25 48725 104 245 0
2022-10-24 48621 172 245 0
2022-10-21 48449 152 245 1
2022-10-20 48297 169 244 1
2022-10-19 48128 216 243 0
2022-10-18 47912 99 243 1
2022-10-17 47813 270 242 1
2022-10-14 47543 110 241 1
2022-10-13 47433 204 240 0
2022-10-12 47229 215 240 1
2022-10-11 47014 358 239 0
2022-10-10 46656 387 239 0
2022-10-07 46269 278 239 0
2022-10-06 45991 230 239 0
2022-10-05 45761 214 239 1
2022-10-04 45547 195 238 1
2022-09-30 45352 120 237 1
2022-09-29 45232 146 236 0
2022-09-28 45086 237 236 0
2022-09-27 44849 330 236 0
2022-09-26 44519 172 236 0
2022-09-23 44347 133 236 0
2022-09-22 44214 63 236 0
2022-09-21 44151 134 236 0
2022-09-20 44017 182 236 1
2022-09-19 43835 167 235 0
2022-09-16 43668 50 235 0
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2022-09-14 43557 81 235 0
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2022-09-01 42892 50 235 0
2022-08-31 42842 60 235 0
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2022-08-26 42639 16 235 0
2022-08-25 42623 27 235 0
2022-08-24 42596 63 235 0
2022-08-23 42533 45 235 0
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2022-08-19 42380 53 235 0
2022-08-18 42327 29 235 0
2022-08-17 42298 98 235 0
2022-08-16 42200 103 235 0
2022-08-15 42097 126 235 0
2022-08-12 41971 65 235 0
2022-08-11 41906 58 235 0
2022-08-10 41848 73 235 1
2022-08-09 41775 124 234 0
2022-08-08 41651 181 234 1
2022-08-05 41470 63 233 0
2022-08-04 41407 67 233 0
2022-08-03 41340 96 233 0
2022-08-02 41244 177 233 0
2022-08-01 41067 201 233 0
2022-07-29 40866 113 233 0
2022-07-28 40753 136 233 1
2022-07-27 40617 261 232 0
2022-07-26 40356 187 232 0
2022-07-25 40169 290 232 0
2022-07-22 39879 153 232 2
2022-07-21 39726 200 230 0
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2022-07-19 39321 222 230 1
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2022-05-30 34001 71 221 0
2022-05-27 33930 62 221 0
2022-05-25 33868 40 221 0
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2022-04-21 30871 274 219 0
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2022-04-19 30337 474 219 4
2022-04-14 29863 223 215 0
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2022-04-11 29123 315 214 2
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2020-12-11 1666 33 49 0
2020-12-10 1633 50 49 6
2020-12-09 1583 32 43 0
2020-12-08 1551 15 43 0
2020-12-07 1536 16 43 0
2020-12-06 1520 13 43 0
2020-12-05 1507 38 43 3
2020-12-04 1469 27 40 0
2020-12-03 1442 63 40 2
2020-12-02 1379 24 38 0
2020-12-01 1355 36 38 3
2020-11-30 1319 33 35 1
2020-11-29 1286 13 34 0
2020-11-28 1273 12 34 0
2020-11-27 1261 30 34 0
2020-11-26 1231 22 34 1
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2020-11-22 1148 27 30 2
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2020-11-19 1079 34 28 3
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2020-03-22 86 6 2 2
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2020-03-14 14 2 0 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: 0:03:28.681467