Germany: LK Donau-Ries (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 10:40:13 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Donau-Ries", weeks=5);
2023-03-07T10:40:28.329496 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 120 120 140 140 160 160 180 180 7-day incidence rate (per 100K people) 109.6 LK Donau-Ries, 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 0.2 0.4 0.6 daily change normalised per 100K 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 cases) Germany-LK Donau-Ries cases daily growth factor Germany-LK Donau-Ries cases daily growth factor (rolling mean) Germany-LK Donau-Ries estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Donau-Ries deaths daily growth factor Germany-LK Donau-Ries deaths daily growth factor (rolling mean) Germany-LK Donau-Ries 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 Donau-Ries doubling time cases (rolling mean) 0 27 54 daily change Germany-LK Donau-Ries new cases (rolling 7d mean) Germany-LK Donau-Ries new cases 0.00 0.27 0.54 0.81 daily change Germany-LK Donau-Ries new deaths (rolling 7d mean) Germany-LK Donau-Ries new deaths 0.000 0.265 0.529 0.794
In [4]:
overview(country="Germany", subregion="LK Donau-Ries");
2023-03-07T10:40:45.515061 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) 109.6 LK Donau-Ries, 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 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Germany-LK Donau-Ries cases daily growth factor Germany-LK Donau-Ries cases daily growth factor (rolling mean) Germany-LK Donau-Ries estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Donau-Ries deaths daily growth factor Germany-LK Donau-Ries deaths daily growth factor (rolling mean) Germany-LK Donau-Ries 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 Donau-Ries doubling time cases (rolling mean) Germany-LK Donau-Ries doubling time deaths (rolling mean) 0 270 540 810 daily change Germany-LK Donau-Ries new cases (rolling 7d mean) Germany-LK Donau-Ries new cases 0.00 1.35 2.70 4.05 5.40 daily change Germany-LK Donau-Ries new deaths (rolling 7d mean) Germany-LK Donau-Ries new deaths 0.0 74.0 148.1 222.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Donau-Ries", dates="2020-03-15:");
2023-03-07T10:43:52.689412 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 Donau-Ries LK Donau-Ries 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 Donau-Ries Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Donau-Ries")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Donau-Ries")
print(f'Population of country="Germany", subregion="LK Donau-Ries": {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 Donau-Ries": 134986 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 73454 25 296 0
2023-03-03 73429 16 296 0
2023-03-02 73413 36 296 0
2023-03-01 73377 36 296 0
2023-02-28 73341 35 296 0
2023-02-27 73306 54 296 0
2023-02-24 73252 21 296 0
2023-02-23 73231 37 296 1
2023-02-22 73194 42 295 0
2023-02-21 73152 43 295 0
2023-02-20 73109 68 295 0
2023-02-18 73041 1 295 0
2023-02-17 73040 37 295 0
2023-02-16 73003 34 295 0
2023-02-15 72969 54 295 0
2023-02-14 72915 53 295 0
2023-02-13 72862 72 295 0
2023-02-10 72790 36 295 1
2023-02-09 72754 21 294 0
2023-02-08 72733 53 294 0
2023-02-07 72680 48 294 0
2023-02-06 72632 65 294 1
2023-02-03 72567 28 293 0
2023-02-02 72539 27 293 0
2023-02-01 72512 59 293 1
2023-01-31 72453 44 292 0
2023-01-30 72409 61 292 2
2023-01-28 72348 4 290 0
2023-01-27 72344 21 290 0
2023-01-26 72323 20 290 0
2023-01-25 72303 36 290 0
2023-01-24 72267 34 290 0
2023-01-23 72233 34 290 0
2023-01-21 72199 2 290 1
2023-01-20 72197 25 289 0
2023-01-19 72172 27 289 0
2023-01-18 72145 17 289 0
2023-01-17 72128 26 289 0
2023-01-16 72102 40 289 0
2023-01-14 72062 1 289 0
2023-01-13 72061 28 289 0
2023-01-12 72033 18 289 1
2023-01-11 72015 28 288 0
2023-01-10 71987 30 288 0
2023-01-09 71957 60 288 1
2023-01-07 71897 1 287 0
2023-01-05 71896 20 287 0
2023-01-04 71876 51 287 0
2023-01-03 71825 39 287 0
2023-01-02 71786 33 287 0
2022-12-30 71753 25 287 0
2022-12-29 71728 24 287 0
2022-12-28 71704 37 287 0
2022-12-27 71667 64 287 1
2022-12-23 71603 30 286 0
2022-12-22 71573 26 286 0
2022-12-21 71547 32 286 0
2022-12-20 71515 62 286 0
2022-12-19 71453 55 286 3
2022-12-16 71398 25 283 0
2022-12-15 71373 25 283 1
2022-12-14 71348 34 282 0
2022-12-13 71314 46 282 0
2022-12-12 71268 48 282 0
2022-12-09 71220 19 282 0
2022-12-08 71201 40 282 0
2022-12-07 71161 35 282 0
2022-12-06 71126 46 282 0
2022-12-05 71080 50 282 0
2022-12-03 71030 1 282 0
2022-12-02 71029 17 282 0
2022-12-01 71012 17 282 0
2022-11-30 70995 33 282 0
2022-11-29 70962 40 282 0
2022-11-28 70922 47 282 0
2022-11-25 70875 33 282 0
2022-11-24 70842 28 282 0
2022-11-23 70814 33 282 0
2022-11-22 70781 48 282 0
2022-11-21 70733 47 282 0
2022-11-19 70686 1 282 0
2022-11-18 70685 59 282 0
2022-11-17 70626 16 282 0
2022-11-15 70610 46 282 1
2022-11-14 70564 55 281 0
2022-11-13 70509 1 281 0
2022-11-11 70508 27 281 0
2022-11-10 70481 53 281 0
2022-11-09 70428 39 281 0
2022-11-08 70389 72 281 0
2022-11-07 70317 93 281 0
2022-11-04 70224 60 281 0
2022-11-03 70164 77 281 0
2022-11-02 70087 96 281 0
2022-11-01 69991 2 281 0
2022-10-31 69989 105 281 0
2022-10-30 69884 3 281 0
2022-10-28 69881 36 281 0
2022-10-27 69845 74 281 0
2022-10-26 69771 96 281 1
2022-10-25 69675 153 280 0
2022-10-24 69522 261 280 0
2022-10-23 69261 1 280 0
2022-10-22 69260 1 280 0
2022-10-21 69259 111 280 0
2022-10-20 69148 125 280 1
2022-10-19 69023 180 279 0
2022-10-18 68843 223 279 0
2022-10-17 68620 301 279 0
2022-10-16 68319 4 279 0
2022-10-15 68315 2 279 0
2022-10-14 68313 209 279 0
2022-10-13 68104 196 279 0
2022-10-12 67908 209 279 0
2022-10-11 67699 368 279 1
2022-10-10 67331 471 278 1
2022-10-09 66860 3 277 0
2022-10-07 66857 191 277 0
2022-10-06 66666 304 277 1
2022-10-05 66362 384 276 3
2022-10-04 65978 332 273 0
2022-10-01 65646 1 273 0
2022-09-30 65645 171 273 0
2022-09-29 65474 160 273 0
2022-09-28 65314 119 273 0
2022-09-27 65195 207 273 0
2022-09-26 64988 168 273 0
2022-09-25 64820 2 273 0
2022-09-23 64818 75 273 0
2022-09-22 64743 98 273 0
2022-09-21 64645 62 273 0
2022-09-20 64583 104 273 1
2022-09-19 64479 91 272 0
2022-09-18 64388 1 272 0
2022-09-16 64387 46 272 0
2022-09-15 64341 35 272 0
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2022-09-13 64257 49 272 0
2022-09-12 64208 69 272 0
2022-09-09 64139 28 272 0
2022-09-08 64111 30 272 0
2022-09-07 64081 49 272 0
2022-09-06 64032 63 272 0
2022-09-05 63969 79 272 1
2022-09-02 63890 27 271 0
2022-09-01 63863 36 271 0
2022-08-31 63827 41 271 0
2022-08-30 63786 91 271 0
2022-08-29 63695 81 271 0
2022-08-26 63614 32 271 0
2022-08-25 63582 43 271 0
2022-08-24 63539 57 271 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: 0:03:54.878564