Germany: LK Altötting (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 10:25:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Altötting", weeks=5);
2023-03-07T10:25:55.116394 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 75 75 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 76.7 LK Altötting, 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 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 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 Altötting cases daily growth factor Germany-LK Altötting cases daily growth factor (rolling mean) Germany-LK Altötting estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 R & growth factor (based on deaths) Germany-LK Altötting deaths daily growth factor Germany-LK Altötting deaths daily growth factor (rolling mean) Germany-LK Altötting 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 Altötting doubling time cases (rolling mean) Germany-LK Altötting doubling time deaths (rolling mean) 0.00 22.42 44.85 daily change Germany-LK Altötting new cases (rolling 7d mean) Germany-LK Altötting new cases 0.000 0.561 1.121 1.682 daily change Germany-LK Altötting new deaths (rolling 7d mean) Germany-LK Altötting new deaths 0.0 103.1 206.1 309.2 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Altötting");
2023-03-07T10:26:14.376112 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) 76.7 LK Altötting, 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 2 4 6 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 Altötting cases daily growth factor Germany-LK Altötting cases daily growth factor (rolling mean) Germany-LK Altötting estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 R & growth factor (based on deaths) Germany-LK Altötting deaths daily growth factor Germany-LK Altötting deaths daily growth factor (rolling mean) Germany-LK Altötting 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 Altötting doubling time cases (rolling mean) Germany-LK Altötting doubling time deaths (rolling mean) 0.0 224.2 448.5 672.7 daily change Germany-LK Altötting new cases (rolling 7d mean) Germany-LK Altötting new cases 0.000 2.242 4.485 6.727 daily change Germany-LK Altötting new deaths (rolling 7d mean) Germany-LK Altötting new deaths 0.0 73.4 146.8 220.1 293.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Altötting", dates="2020-03-15:");
2023-03-07T10:28:22.051967 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 Altötting LK Altötting 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 Altötting Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Altötting")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Altötting")
print(f'Population of country="Germany", subregion="LK Altötting": {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 Altötting": 112116 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 62027 26 416 0
2023-03-03 62001 15 416 0
2023-03-02 61986 15 416 0
2023-03-01 61971 14 416 1
2023-02-28 61957 16 415 0
2023-02-27 61941 44 415 0
2023-02-24 61897 13 415 0
2023-02-23 61884 28 415 0
2023-02-22 61856 45 415 1
2023-02-21 61811 34 414 2
2023-02-20 61777 38 412 0
2023-02-17 61739 14 412 1
2023-02-16 61725 35 411 0
2023-02-15 61690 29 411 0
2023-02-14 61661 36 411 0
2023-02-13 61625 62 411 1
2023-02-10 61563 12 410 1
2023-02-09 61551 30 409 2
2023-02-08 61521 50 407 0
2023-02-07 61471 51 407 0
2023-02-06 61420 43 407 0
2023-02-03 61377 16 407 0
2023-02-02 61361 27 407 0
2023-02-01 61334 33 407 0
2023-01-31 61301 45 407 2
2023-01-30 61256 43 405 0
2023-01-27 61213 8 405 0
2023-01-26 61205 31 405 1
2023-01-25 61174 27 404 0
2023-01-24 61147 44 404 2
2023-01-23 61103 51 402 2
2023-01-20 61052 12 400 1
2023-01-19 61040 19 399 2
2023-01-18 61021 17 397 0
2023-01-17 61004 20 397 0
2023-01-16 60984 44 397 0
2023-01-13 60940 15 397 1
2023-01-12 60925 21 396 0
2023-01-11 60904 18 396 0
2023-01-10 60886 26 396 0
2023-01-09 60860 52 396 0
2023-01-05 60808 19 396 1
2023-01-04 60789 20 395 2
2023-01-03 60769 35 393 1
2023-01-02 60734 50 392 1
2022-12-30 60684 10 391 0
2022-12-29 60674 16 391 0
2022-12-28 60658 38 391 0
2022-12-27 60620 66 391 1
2022-12-23 60554 20 390 0
2022-12-22 60534 15 390 0
2022-12-21 60519 35 390 0
2022-12-20 60484 31 390 0
2022-12-19 60453 67 390 0
2022-12-16 60386 9 390 0
2022-12-15 60377 24 390 0
2022-12-14 60353 44 390 3
2022-12-13 60309 31 387 0
2022-12-12 60278 68 387 2
2022-12-09 60210 10 385 0
2022-12-08 60200 25 385 1
2022-12-07 60175 36 384 0
2022-12-06 60139 33 384 0
2022-12-05 60106 44 384 0
2022-12-02 60062 19 384 0
2022-12-01 60043 32 384 0
2022-11-30 60011 33 384 1
2022-11-29 59978 25 383 0
2022-11-28 59953 53 383 4
2022-11-25 59900 11 379 0
2022-11-24 59889 14 379 1
2022-11-23 59875 21 378 0
2022-11-22 59854 31 378 1
2022-11-21 59823 38 377 0
2022-11-18 59785 9 377 0
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2022-11-10 59630 37 377 1
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2022-10-31 59319 137 376 1
2022-10-28 59182 42 375 0
2022-10-27 59140 70 375 0
2022-10-26 59070 70 375 0
2022-10-25 59000 136 375 0
2022-10-24 58864 173 375 1
2022-10-21 58691 47 374 0
2022-10-20 58644 126 374 1
2022-10-19 58518 138 373 0
2022-10-18 58380 190 373 2
2022-10-17 58190 355 371 0
2022-10-14 57835 89 371 1
2022-10-13 57746 256 370 0
2022-10-12 57490 263 370 4
2022-10-11 57227 263 366 1
2022-10-10 56964 448 365 1
2022-10-07 56516 172 364 0
2022-10-06 56344 322 364 0
2022-10-05 56022 449 364 1
2022-10-04 55573 395 363 1
2022-09-30 55178 89 362 0
2022-09-29 55089 181 362 0
2022-09-28 54908 202 362 0
2022-09-27 54706 211 362 1
2022-09-26 54495 182 361 0
2022-09-23 54313 97 361 0
2022-09-22 54216 60 361 0
2022-09-21 54156 87 361 0
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2022-09-16 53863 61 361 0
2022-09-15 53802 89 361 0
2022-09-14 53713 47 361 0
2022-09-13 53666 72 361 0
2022-09-12 53594 106 361 1
2022-09-09 53488 50 360 0
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2022-09-07 53389 33 360 0
2022-09-06 53356 55 360 1
2022-09-05 53301 62 359 0
2022-09-02 53239 32 359 0
2022-09-01 53207 37 359 0
2022-08-31 53170 48 359 1
2022-08-30 53122 57 358 0
2022-08-29 53065 76 358 0
2022-08-26 52989 30 358 0
2022-08-25 52959 27 358 1
2022-08-24 52932 35 357 0
2022-08-23 52897 19 357 0
2022-08-22 52878 43 357 0
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2022-08-08 52369 121 357 1
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2022-08-04 52193 67 356 0
2022-08-03 52126 85 356 0
2022-08-02 52041 90 356 1
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2022-05-07 44044 1 332 0
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2022-05-05 43938 131 331 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: 0:02:55.526139