Germany: LK Hochtaunuskreis (Hessen)¶

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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 12:36:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Hochtaunuskreis", weeks=5);
2023-03-07T12:36:57.255922 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 73.4 LK Hochtaunuskreis, 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 0.0 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 Hochtaunuskreis cases daily growth factor Germany-LK Hochtaunuskreis cases daily growth factor (rolling mean) Germany-LK Hochtaunuskreis estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Hochtaunuskreis deaths daily growth factor Germany-LK Hochtaunuskreis deaths daily growth factor (rolling mean) Germany-LK Hochtaunuskreis estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Hochtaunuskreis doubling time cases (rolling mean) 0.0 23.7 47.4 71.1 94.8 daily change Germany-LK Hochtaunuskreis new cases (rolling 7d mean) Germany-LK Hochtaunuskreis new cases 0.000 0.237 0.474 0.711 0.948 daily change Germany-LK Hochtaunuskreis new deaths (rolling 7d mean) Germany-LK Hochtaunuskreis new deaths 0.000 0.212 0.424 0.636 0.848
In [4]:
overview(country="Germany", subregion="LK Hochtaunuskreis");
2023-03-07T12:37:15.066428 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 7-day incidence rate (per 100K people) 73.4 LK Hochtaunuskreis, 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 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 Hochtaunuskreis cases daily growth factor Germany-LK Hochtaunuskreis cases daily growth factor (rolling mean) Germany-LK Hochtaunuskreis estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Hochtaunuskreis deaths daily growth factor Germany-LK Hochtaunuskreis deaths daily growth factor (rolling mean) Germany-LK Hochtaunuskreis 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 Hochtaunuskreis doubling time cases (rolling mean) Germany-LK Hochtaunuskreis doubling time deaths (rolling mean) 0 474 948 daily change Germany-LK Hochtaunuskreis new cases (rolling 7d mean) Germany-LK Hochtaunuskreis new cases 0.000 2.370 4.741 7.111 daily change Germany-LK Hochtaunuskreis new deaths (rolling 7d mean) Germany-LK Hochtaunuskreis new deaths 0.0 55.4 110.8 166.2 221.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Hochtaunuskreis", dates="2020-03-15:");
2023-03-07T12:40:03.989954 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 Hochtaunuskreis LK Hochtaunuskreis 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 Hochtaunuskreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Hochtaunuskreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Hochtaunuskreis")
print(f'Population of country="Germany", subregion="LK Hochtaunuskreis": {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 Hochtaunuskreis": 237041 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 104517 29 321 0
2023-03-03 104488 16 321 0
2023-03-02 104472 17 321 0
2023-03-01 104455 41 321 0
2023-02-28 104414 71 321 1
2023-02-27 104343 100 320 1
2023-02-24 104243 87 319 0
2023-02-23 104156 44 319 0
2023-02-22 104112 78 319 0
2023-02-21 104034 57 319 0
2023-02-20 103977 90 319 0
2023-02-17 103887 61 319 1
2023-02-16 103826 42 318 0
2023-02-15 103784 74 318 0
2023-02-14 103710 81 318 0
2023-02-13 103629 72 318 0
2023-02-10 103557 48 318 0
2023-02-09 103509 42 318 0
2023-02-08 103467 44 318 0
2023-02-07 103423 45 318 0
2023-02-06 103378 86 318 0
2023-02-03 103292 46 318 0
2023-02-02 103246 30 318 0
2023-02-01 103216 50 318 0
2023-01-31 103166 62 318 0
2023-01-30 103104 74 318 0
2023-01-27 103030 26 318 0
2023-01-26 103004 39 318 0
2023-01-25 102965 30 318 1
2023-01-24 102935 45 317 0
2023-01-23 102890 54 317 0
2023-01-20 102836 19 317 0
2023-01-19 102817 28 317 1
2023-01-18 102789 31 316 0
2023-01-17 102758 35 316 0
2023-01-16 102723 52 316 1
2023-01-13 102671 32 315 0
2023-01-12 102639 24 315 0
2023-01-11 102615 82 315 1
2023-01-10 102533 60 314 0
2023-01-09 102473 85 314 0
2023-01-06 102388 79 314 0
2023-01-05 102309 55 314 0
2023-01-04 102254 72 314 1
2023-01-03 102182 52 313 0
2023-01-02 102130 120 313 1
2022-12-30 102010 74 312 0
2022-12-29 101936 129 312 0
2022-12-28 101807 72 312 0
2022-12-27 101735 241 312 0
2022-12-23 101494 123 312 0
2022-12-22 101371 111 312 0
2022-12-21 101260 109 312 0
2022-12-20 101151 134 312 0
2022-12-19 101017 139 312 0
2022-12-16 100878 102 312 1
2022-12-15 100776 80 311 0
2022-12-14 100696 98 311 0
2022-12-13 100598 126 311 3
2022-12-12 100472 111 308 0
2022-12-09 100361 88 308 1
2022-12-08 100273 86 307 0
2022-12-07 100187 71 307 0
2022-12-06 100116 109 307 1
2022-12-05 100007 150 306 0
2022-12-02 99857 69 306 0
2022-12-01 99788 90 306 0
2022-11-30 99698 61 306 0
2022-11-29 99637 80 306 0
2022-11-28 99557 117 306 1
2022-11-25 99440 84 305 1
2022-11-24 99356 61 304 0
2022-11-23 99295 110 304 1
2022-11-22 99185 100 303 0
2022-11-21 99085 123 303 0
2022-11-18 98962 83 303 0
2022-11-17 98879 84 303 0
2022-11-16 98795 109 303 0
2022-11-15 98686 96 303 0
2022-11-14 98590 171 303 0
2022-11-11 98419 71 303 0
2022-11-10 98348 126 303 0
2022-11-09 98222 71 303 0
2022-11-08 98151 146 303 0
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2022-11-04 97818 70 303 0
2022-11-03 97748 116 303 0
2022-11-02 97632 145 303 0
2022-11-01 97487 176 303 0
2022-10-31 97311 291 303 1
2022-10-28 97020 122 302 0
2022-10-27 96898 177 302 1
2022-10-26 96721 199 301 0
2022-10-25 96522 217 301 0
2022-10-24 96305 371 301 1
2022-10-21 95934 231 300 2
2022-10-20 95703 224 298 0
2022-10-19 95479 335 298 0
2022-10-18 95144 390 298 0
2022-10-17 94754 637 298 0
2022-10-14 94117 277 298 0
2022-10-13 93840 334 298 1
2022-10-12 93506 357 297 1
2022-10-11 93149 532 296 1
2022-10-10 92617 628 295 2
2022-10-07 91989 331 293 1
2022-10-06 91658 463 292 0
2022-10-05 91195 659 292 1
2022-10-04 90536 890 291 1
2022-09-30 89646 252 290 0
2022-09-29 89394 320 290 1
2022-09-28 89074 369 289 0
2022-09-27 88705 505 289 1
2022-09-26 88200 448 288 0
2022-09-23 87752 189 288 0
2022-09-22 87563 254 288 0
2022-09-21 87309 259 288 0
2022-09-20 87050 394 288 0
2022-09-19 86656 385 288 1
2022-09-16 86271 182 287 0
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2022-09-13 85756 154 287 1
2022-09-12 85602 202 286 0
2022-09-09 85400 83 286 0
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2022-09-07 85206 99 285 0
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2022-09-05 84966 159 285 0
2022-09-02 84807 85 285 0
2022-09-01 84722 82 285 0
2022-08-31 84640 118 285 1
2022-08-30 84522 131 284 1
2022-08-29 84391 239 283 0
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2022-08-25 84074 74 283 0
2022-08-24 84000 74 283 0
2022-08-23 83926 81 283 0
2022-08-22 83845 167 283 0
2022-08-19 83678 106 283 0
2022-08-18 83572 175 283 0
2022-08-17 83397 146 283 2
2022-08-16 83251 164 281 0
2022-08-15 83087 224 281 0
2022-08-12 82863 174 281 0
2022-08-11 82689 117 281 0
2022-08-10 82572 176 281 1
2022-08-09 82396 221 280 0
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2022-08-04 81743 191 279 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: 0:03:44.777087