Germany: LK Günzburg (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 10:55:20 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Günzburg", weeks=5);
2023-03-07T10:55:38.705267 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) 64.6 LK Günzburg, 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 1.5 2.0 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 Günzburg cases daily growth factor Germany-LK Günzburg cases daily growth factor (rolling mean) Germany-LK Günzburg 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 Günzburg deaths daily growth factor Germany-LK Günzburg deaths daily growth factor (rolling mean) Germany-LK Günzburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-LK Günzburg doubling time cases (rolling mean) Germany-LK Günzburg doubling time deaths (rolling mean) 0.0 25.7 51.4 77.1 daily change Germany-LK Günzburg new cases (rolling 7d mean) Germany-LK Günzburg new cases 0.000 0.642 1.284 1.927 2.569 daily change Germany-LK Günzburg new deaths (rolling 7d mean) Germany-LK Günzburg new deaths 0.0 66.5 133.0 199.5 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Günzburg");
2023-03-07T10:55:54.427333 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) 64.6 LK Günzburg, 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 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 Günzburg cases daily growth factor Germany-LK Günzburg cases daily growth factor (rolling mean) Germany-LK Günzburg 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 Günzburg deaths daily growth factor Germany-LK Günzburg deaths daily growth factor (rolling mean) Germany-LK Günzburg 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 Günzburg doubling time cases (rolling mean) Germany-LK Günzburg doubling time deaths (rolling mean) 0.0 256.9 513.7 daily change Germany-LK Günzburg new cases (rolling 7d mean) Germany-LK Günzburg new cases 0.000 2.569 5.137 daily change Germany-LK Günzburg new deaths (rolling 7d mean) Germany-LK Günzburg new deaths 0.0 55.4 110.7 166.1 221.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Günzburg", dates="2020-03-15:");
2023-03-07T10:58:18.608918 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 Günzburg LK Günzburg 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 Günzburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Günzburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Günzburg")
print(f'Population of country="Germany", subregion="LK Günzburg": {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 Günzburg": 128436 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 65564 1 258 0
2023-03-05 65563 5 258 0
2023-03-04 65558 3 258 0
2023-03-03 65555 11 258 0
2023-03-02 65544 10 258 0
2023-03-01 65534 24 258 0
2023-02-28 65510 29 258 0
2023-02-27 65481 49 258 0
2023-02-26 65432 6 258 0
2023-02-25 65426 11 258 0
2023-02-24 65415 40 258 0
2023-02-23 65375 30 258 2
2023-02-22 65345 43 256 0
2023-02-21 65302 39 256 1
2023-02-20 65263 63 255 0
2023-02-19 65200 4 255 1
2023-02-18 65196 13 254 0
2023-02-17 65183 31 254 0
2023-02-16 65152 36 254 0
2023-02-15 65116 36 254 0
2023-02-14 65080 59 254 1
2023-02-13 65021 83 253 1
2023-02-12 64938 10 252 0
2023-02-11 64928 19 252 0
2023-02-10 64909 67 252 0
2023-02-09 64842 46 252 3
2023-02-08 64796 55 249 0
2023-02-07 64741 78 249 0
2023-02-06 64663 97 249 0
2023-02-05 64566 13 249 0
2023-02-04 64553 14 249 0
2023-02-03 64539 54 249 0
2023-02-02 64485 83 249 0
2023-02-01 64402 47 249 1
2023-01-31 64355 47 248 1
2023-01-30 64308 56 247 1
2023-01-29 64252 3 246 0
2023-01-28 64249 11 246 1
2023-01-27 64238 31 245 1
2023-01-26 64207 27 244 0
2023-01-25 64180 30 244 0
2023-01-24 64150 32 244 0
2023-01-23 64118 48 244 0
2023-01-22 64070 4 244 0
2023-01-21 64066 11 244 0
2023-01-20 64055 21 244 0
2023-01-19 64034 24 244 1
2023-01-18 64010 17 243 0
2023-01-17 63993 20 243 0
2023-01-16 63973 24 243 1
2023-01-15 63949 4 242 0
2023-01-14 63945 11 242 1
2023-01-13 63934 7 241 1
2023-01-12 63927 10 240 0
2023-01-11 63917 13 240 0
2023-01-10 63904 19 240 0
2023-01-09 63885 49 240 0
2023-01-08 63836 2 240 0
2023-01-07 63834 8 240 1
2023-01-06 63826 10 239 0
2023-01-05 63816 17 239 0
2023-01-04 63799 28 239 0
2023-01-03 63771 26 239 0
2023-01-02 63745 19 239 0
2023-01-01 63726 3 239 0
2022-12-31 63723 15 239 0
2022-12-30 63708 30 239 0
2022-12-29 63678 21 239 1
2022-12-28 63657 30 238 0
2022-12-27 63627 25 238 0
2022-12-26 63602 8 238 0
2022-12-25 63594 4 238 0
2022-12-24 63590 14 238 1
2022-12-23 63576 19 237 0
2022-12-22 63557 29 237 0
2022-12-21 63528 41 237 0
2022-12-20 63487 27 237 1
2022-12-19 63460 38 236 0
2022-12-18 63422 3 236 0
2022-12-17 63419 10 236 0
2022-12-16 63409 19 236 0
2022-12-15 63390 31 236 1
2022-12-14 63359 42 235 1
2022-12-13 63317 19 234 0
2022-12-12 63298 31 234 0
2022-12-11 63267 6 234 0
2022-12-10 63261 5 234 0
2022-12-09 63256 21 234 0
2022-12-08 63235 22 234 0
2022-12-07 63213 12 234 0
2022-12-06 63201 26 234 0
2022-12-05 63175 24 234 0
2022-12-04 63151 1 234 0
2022-12-03 63150 5 234 0
2022-12-02 63145 16 234 0
2022-12-01 63129 27 234 0
2022-11-30 63102 27 234 1
2022-11-29 63075 21 233 0
2022-11-28 63054 36 233 0
2022-11-27 63018 3 233 0
2022-11-26 63015 15 233 1
2022-11-25 63000 23 232 1
2022-11-24 62977 15 231 0
2022-11-23 62962 24 231 0
2022-11-22 62938 26 231 0
2022-11-21 62912 19 231 0
2022-11-20 62893 1 231 0
2022-11-19 62892 6 231 0
2022-11-18 62886 22 231 0
2022-11-17 62864 9 231 0
2022-11-16 62855 24 231 1
2022-11-15 62831 28 230 0
2022-11-14 62803 33 230 0
2022-11-13 62770 2 230 0
2022-11-12 62768 5 230 0
2022-11-11 62763 24 230 0
2022-11-10 62739 31 230 0
2022-11-09 62708 26 230 2
2022-11-08 62682 53 228 0
2022-11-07 62629 58 228 0
2022-11-06 62571 8 228 0
2022-11-05 62563 13 228 0
2022-11-04 62550 33 228 0
2022-11-03 62517 45 228 0
2022-11-02 62472 57 228 0
2022-11-01 62415 29 228 0
2022-10-31 62386 56 228 2
2022-10-30 62330 7 226 0
2022-10-29 62323 19 226 0
2022-10-28 62304 62 226 0
2022-10-27 62242 66 226 0
2022-10-26 62176 75 226 0
2022-10-25 62101 98 226 0
2022-10-24 62003 142 226 0
2022-10-23 61861 9 226 0
2022-10-22 61852 22 226 1
2022-10-21 61830 104 225 0
2022-10-20 61726 118 225 0
2022-10-19 61608 123 225 0
2022-10-18 61485 197 225 0
2022-10-17 61288 226 225 0
2022-10-16 61062 21 225 0
2022-10-15 61041 90 225 0
2022-10-14 60951 124 225 0
2022-10-13 60827 135 225 0
2022-10-12 60692 181 225 0
2022-10-11 60511 259 225 2
2022-10-10 60252 309 223 0
2022-10-09 59943 14 223 0
2022-10-08 59929 112 223 2
2022-10-07 59817 180 221 0
2022-10-06 59637 236 221 1
2022-10-05 59401 414 220 0
2022-10-04 58987 205 220 0
2022-10-03 58782 139 220 0
2022-10-02 58643 16 220 0
2022-10-01 58627 63 220 1
2022-09-30 58564 162 219 0
2022-09-29 58402 171 219 0
2022-09-28 58231 186 219 0
2022-09-27 58045 219 219 0
2022-09-26 57826 211 219 0
2022-09-25 57615 8 219 0
2022-09-24 57607 23 219 0
2022-09-23 57584 144 219 0
2022-09-22 57440 128 219 0
2022-09-21 57312 154 219 0
2022-09-20 57158 115 219 0
2022-09-19 57043 171 219 1
2022-09-18 56872 8 218 0
2022-09-17 56864 39 218 0
2022-09-16 56825 116 218 0
2022-09-15 56709 86 218 0
2022-09-14 56623 83 218 0
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2022-09-12 56448 81 218 2
2022-09-11 56367 5 216 0
2022-09-10 56362 18 216 0
2022-09-09 56344 38 216 0
2022-09-08 56306 34 216 0
2022-09-07 56272 33 216 0
2022-09-06 56239 53 216 0
2022-09-05 56186 61 216 1
2022-09-04 56125 8 215 0
2022-09-03 56117 13 215 0
2022-09-02 56104 40 215 0
2022-09-01 56064 26 215 0
2022-08-31 56038 45 215 0
2022-08-30 55993 58 215 0
2022-08-29 55935 67 215 1
2022-08-28 55868 2 214 0
2022-08-27 55866 20 214 0
2022-08-26 55846 63 214 0
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2022-08-10 55196 60 214 0
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2022-08-05 54980 78 214 0
2022-08-04 54902 54 214 0
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2022-08-01 54693 133 214 0
2022-07-31 54560 6 214 0
2022-07-30 54554 24 214 0
2022-07-29 54530 102 214 0
2022-07-28 54428 97 214 0
2022-07-27 54331 157 214 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: 0:03:10.529304