Germany: LK Rottal-Inn (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 11:22:07 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rottal-Inn", weeks=5);
2023-03-07T11:22:22.572417 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 7-day incidence rate (per 100K people) 58.1 LK Rottal-Inn, 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 0.2 0.4 0.6 0.8 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Rottal-Inn cases daily growth factor Germany-LK Rottal-Inn cases daily growth factor (rolling mean) Germany-LK Rottal-Inn estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Rottal-Inn deaths daily growth factor Germany-LK Rottal-Inn deaths daily growth factor (rolling mean) Germany-LK Rottal-Inn 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 Rottal-Inn doubling time cases (rolling mean) 0.00 12.23 24.45 36.68 48.90 daily change Germany-LK Rottal-Inn new cases (rolling 7d mean) Germany-LK Rottal-Inn new cases 0.000 0.245 0.489 0.734 0.978 daily change Germany-LK Rottal-Inn new deaths (rolling 7d mean) Germany-LK Rottal-Inn new deaths 0.000 0.272 0.543 0.815
In [4]:
overview(country="Germany", subregion="LK Rottal-Inn");
2023-03-07T11:22:36.929373 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) 58.1 LK Rottal-Inn, 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 0 200 400 600 800 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 Rottal-Inn cases daily growth factor Germany-LK Rottal-Inn cases daily growth factor (rolling mean) Germany-LK Rottal-Inn estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Rottal-Inn deaths daily growth factor Germany-LK Rottal-Inn deaths daily growth factor (rolling mean) Germany-LK Rottal-Inn 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 cases doubling time [days] Germany-LK Rottal-Inn doubling time cases (rolling mean) Germany-LK Rottal-Inn doubling time deaths (rolling mean) 0 245 489 734 978 daily change Germany-LK Rottal-Inn new cases (rolling 7d mean) Germany-LK Rottal-Inn new cases 0.000 2.445 4.890 daily change Germany-LK Rottal-Inn new deaths (rolling 7d mean) Germany-LK Rottal-Inn new deaths 0.0 108.7 217.3 326.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rottal-Inn", dates="2020-03-15:");
2023-03-07T11:25:17.435348 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 Rottal-Inn LK Rottal-Inn 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 Rottal-Inn Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rottal-Inn")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rottal-Inn")
print(f'Population of country="Germany", subregion="LK Rottal-Inn": {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 Rottal-Inn": 122252 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 65182 22 380 0
2023-03-03 65160 8 380 0
2023-03-02 65152 6 380 0
2023-03-01 65146 22 380 0
2023-02-28 65124 13 380 0
2023-02-27 65111 34 380 0
2023-02-24 65077 22 380 0
2023-02-23 65055 17 380 0
2023-02-22 65038 50 380 1
2023-02-21 64988 14 379 0
2023-02-20 64974 30 379 1
2023-02-17 64944 15 378 0
2023-02-16 64929 19 378 0
2023-02-15 64910 15 378 0
2023-02-14 64895 36 378 0
2023-02-13 64859 32 378 0
2023-02-10 64827 17 378 0
2023-02-09 64810 20 378 0
2023-02-08 64790 28 378 0
2023-02-07 64762 29 378 0
2023-02-06 64733 31 378 1
2023-02-03 64702 14 377 0
2023-02-02 64688 14 377 0
2023-02-01 64674 36 377 0
2023-01-31 64638 22 377 0
2023-01-30 64616 19 377 0
2023-01-27 64597 17 377 0
2023-01-26 64580 13 377 0
2023-01-25 64567 25 377 0
2023-01-24 64542 19 377 0
2023-01-23 64523 21 377 0
2023-01-20 64502 16 377 0
2023-01-19 64486 6 377 0
2023-01-18 64480 17 377 0
2023-01-17 64463 14 377 0
2023-01-16 64449 14 377 0
2023-01-13 64435 10 377 0
2023-01-12 64425 17 377 0
2023-01-11 64408 10 377 0
2023-01-10 64398 17 377 0
2023-01-09 64381 26 377 0
2023-01-05 64355 6 377 0
2023-01-04 64349 27 377 0
2023-01-03 64322 6 377 0
2023-01-02 64316 12 377 0
2022-12-30 64304 12 377 0
2022-12-29 64292 15 377 0
2022-12-28 64277 24 377 0
2022-12-27 64253 34 377 0
2022-12-23 64219 10 377 0
2022-12-22 64209 8 377 0
2022-12-21 64201 23 377 0
2022-12-20 64178 31 377 0
2022-12-19 64147 10 377 0
2022-12-16 64137 20 377 0
2022-12-15 64117 25 377 0
2022-12-14 64092 9 377 0
2022-12-13 64083 33 377 0
2022-12-12 64050 15 377 0
2022-12-09 64035 22 377 0
2022-12-08 64013 11 377 0
2022-12-07 64002 11 377 0
2022-12-06 63991 21 377 0
2022-12-05 63970 18 377 0
2022-12-02 63952 15 377 0
2022-12-01 63937 18 377 0
2022-11-30 63919 13 377 0
2022-11-29 63906 33 377 0
2022-11-28 63873 24 377 0
2022-11-25 63849 17 377 0
2022-11-24 63832 14 377 0
2022-11-23 63818 12 377 0
2022-11-22 63806 30 377 0
2022-11-21 63776 19 377 0
2022-11-18 63757 10 377 0
2022-11-17 63747 11 377 0
2022-11-16 63736 13 377 0
2022-11-15 63723 40 377 0
2022-11-14 63683 22 377 0
2022-11-11 63661 13 377 0
2022-11-10 63648 20 377 0
2022-11-09 63628 26 377 0
2022-11-08 63602 67 377 1
2022-11-07 63535 53 376 1
2022-11-04 63482 47 375 0
2022-11-03 63435 39 375 0
2022-11-02 63396 51 375 0
2022-10-31 63345 48 375 0
2022-10-28 63297 56 375 0
2022-10-27 63241 59 375 0
2022-10-26 63182 90 375 0
2022-10-25 63092 121 375 1
2022-10-24 62971 97 374 0
2022-10-21 62874 61 374 0
2022-10-20 62813 93 374 0
2022-10-19 62720 111 374 1
2022-10-18 62609 222 373 0
2022-10-17 62387 237 373 1
2022-10-14 62150 102 372 1
2022-10-13 62048 157 371 0
2022-10-12 61891 242 371 1
2022-10-11 61649 294 370 0
2022-10-10 61355 272 370 0
2022-10-07 61083 187 370 1
2022-10-06 60896 349 369 0
2022-10-05 60547 400 369 0
2022-10-04 60147 241 369 0
2022-09-30 59906 133 369 0
2022-09-29 59773 222 369 0
2022-09-28 59551 229 369 0
2022-09-27 59322 211 369 0
2022-09-26 59111 146 369 0
2022-09-23 58965 132 369 0
2022-09-22 58833 127 369 0
2022-09-21 58706 99 369 0
2022-09-20 58607 172 369 0
2022-09-19 58435 102 369 0
2022-09-16 58333 92 369 0
2022-09-15 58241 96 369 0
2022-09-14 58145 79 369 0
2022-09-13 58066 170 369 1
2022-09-12 57896 72 368 0
2022-09-09 57824 49 368 0
2022-09-08 57775 52 368 0
2022-09-07 57723 40 368 0
2022-09-06 57683 58 368 0
2022-09-05 57625 33 368 0
2022-09-02 57592 34 368 0
2022-09-01 57558 30 368 1
2022-08-31 57528 30 367 0
2022-08-30 57498 66 367 0
2022-08-29 57432 50 367 0
2022-08-26 57382 36 367 0
2022-08-25 57346 33 367 0
2022-08-24 57313 33 367 0
2022-08-23 57280 71 367 1
2022-08-22 57209 46 366 0
2022-08-19 57163 39 366 0
2022-08-18 57124 39 366 0
2022-08-17 57085 79 366 0
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2022-08-12 56919 53 366 0
2022-08-11 56866 80 366 0
2022-08-10 56786 34 366 0
2022-08-09 56752 108 366 0
2022-08-08 56644 63 366 1
2022-08-05 56581 51 365 0
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2022-08-03 56458 74 365 1
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2022-07-26 55830 283 363 1
2022-07-25 55547 172 362 2
2022-07-22 55375 162 360 0
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2022-05-03 49383 148 351 2
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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: 0:03:27.691568