Germany: LK Ebersberg (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 10:40:41 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ebersberg", weeks=5);
2023-03-07T10:40:54.595752 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 70 70 80 80 90 90 7-day incidence rate (per 100K people) 54.6 LK Ebersberg, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Ebersberg new deaths (rolling 7d mean) Germany-LK Ebersberg new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Ebersberg cases daily growth factor Germany-LK Ebersberg cases daily growth factor (rolling mean) Germany-LK Ebersberg estimated R (using cases) 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 deaths) Germany-LK Ebersberg deaths daily growth factor Germany-LK Ebersberg deaths daily growth factor (rolling mean) Germany-LK Ebersberg 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 Ebersberg doubling time cases (rolling mean) 0.00 14.46 28.91 daily change Germany-LK Ebersberg new cases (rolling 7d mean) Germany-LK Ebersberg new cases 0.000 0.262 0.524 0.787
In [4]:
overview(country="Germany", subregion="LK Ebersberg");
2023-03-07T10:41:11.555241 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) 54.6 LK Ebersberg, 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 6 8 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 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Ebersberg cases daily growth factor Germany-LK Ebersberg cases daily growth factor (rolling mean) Germany-LK Ebersberg estimated R (using cases) 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 deaths) Germany-LK Ebersberg deaths daily growth factor Germany-LK Ebersberg deaths daily growth factor (rolling mean) Germany-LK Ebersberg 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 Ebersberg doubling time cases (rolling mean) Germany-LK Ebersberg doubling time deaths (rolling mean) 0.0 289.1 578.2 daily change Germany-LK Ebersberg new cases (rolling 7d mean) Germany-LK Ebersberg new cases 0.00 2.89 5.78 8.67 11.56 daily change Germany-LK Ebersberg new deaths (rolling 7d mean) Germany-LK Ebersberg new deaths 0.0 73.7 147.5 221.2 295.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ebersberg", dates="2020-03-15:");
2023-03-07T10:43:45.158670 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 Ebersberg LK Ebersberg 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 Ebersberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ebersberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ebersberg")
print(f'Population of country="Germany", subregion="LK Ebersberg": {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 Ebersberg": 144562 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 74807 16 314 0
2023-03-03 74791 3 314 0
2023-03-02 74788 9 314 0
2023-03-01 74779 31 314 0
2023-02-28 74748 20 314 0
2023-02-27 74728 17 314 0
2023-02-24 74711 14 314 0
2023-02-23 74697 21 314 0
2023-02-22 74676 27 314 0
2023-02-21 74649 15 314 0
2023-02-20 74634 37 314 0
2023-02-17 74597 16 314 0
2023-02-16 74581 23 314 0
2023-02-15 74558 13 314 0
2023-02-14 74545 19 314 0
2023-02-13 74526 20 314 0
2023-02-10 74506 25 314 0
2023-02-09 74481 14 314 0
2023-02-08 74467 24 314 0
2023-02-07 74443 19 314 0
2023-02-06 74424 27 314 0
2023-02-03 74397 13 314 0
2023-02-02 74384 21 314 0
2023-02-01 74363 27 314 0
2023-01-31 74336 40 314 0
2023-01-30 74296 34 314 0
2023-01-27 74262 18 314 0
2023-01-26 74244 14 314 0
2023-01-25 74230 12 314 0
2023-01-24 74218 8 314 0
2023-01-23 74210 31 314 0
2023-01-20 74179 6 314 0
2023-01-19 74173 23 314 0
2023-01-18 74150 21 314 0
2023-01-17 74129 23 314 0
2023-01-16 74106 30 314 0
2023-01-13 74076 11 314 0
2023-01-12 74065 14 314 0
2023-01-11 74051 24 314 0
2023-01-10 74027 22 314 0
2023-01-09 74005 48 314 0
2023-01-05 73957 9 314 0
2023-01-04 73948 14 314 0
2023-01-03 73934 24 314 0
2023-01-02 73910 35 314 0
2022-12-30 73875 23 314 0
2022-12-29 73852 41 314 0
2022-12-28 73811 47 314 0
2022-12-27 73764 48 314 0
2022-12-23 73716 9 314 0
2022-12-22 73707 20 314 0
2022-12-21 73687 25 314 0
2022-12-20 73662 20 314 0
2022-12-19 73642 57 314 1
2022-12-16 73585 22 313 0
2022-12-15 73563 19 313 0
2022-12-14 73544 23 313 1
2022-12-13 73521 29 312 0
2022-12-12 73492 30 312 0
2022-12-09 73462 25 312 0
2022-12-08 73437 22 312 0
2022-12-07 73415 37 312 0
2022-12-06 73378 19 312 0
2022-12-05 73359 42 312 0
2022-12-02 73317 22 312 0
2022-12-01 73295 30 312 0
2022-11-30 73265 22 312 0
2022-11-29 73243 30 312 0
2022-11-28 73213 24 312 0
2022-11-25 73189 28 312 0
2022-11-24 73161 28 312 0
2022-11-23 73133 32 312 0
2022-11-22 73101 22 312 0
2022-11-21 73079 39 312 0
2022-11-18 73040 16 312 0
2022-11-17 73024 25 312 0
2022-11-16 72999 15 312 0
2022-11-15 72984 24 312 0
2022-11-14 72960 37 312 0
2022-11-11 72923 16 312 0
2022-11-10 72907 35 312 0
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2022-11-08 72841 40 312 0
2022-11-07 72801 69 312 0
2022-11-04 72732 43 312 0
2022-11-03 72689 64 312 0
2022-11-02 72625 74 312 0
2022-10-31 72551 85 312 0
2022-10-28 72466 39 312 2
2022-10-27 72427 46 310 1
2022-10-26 72381 56 309 0
2022-10-25 72325 84 309 0
2022-10-24 72241 125 309 0
2022-10-21 72116 67 309 1
2022-10-20 72049 86 308 0
2022-10-19 71963 151 308 1
2022-10-18 71812 168 307 1
2022-10-17 71644 329 306 1
2022-10-14 71315 117 305 0
2022-10-13 71198 205 305 0
2022-10-12 70993 267 305 1
2022-10-11 70726 399 304 1
2022-10-10 70327 482 303 3
2022-10-07 69845 234 300 1
2022-10-06 69611 426 299 0
2022-10-05 69185 634 299 0
2022-10-04 68551 524 299 1
2022-09-30 68027 243 298 0
2022-09-29 67784 271 298 0
2022-09-28 67513 279 298 0
2022-09-27 67234 324 298 0
2022-09-26 66910 338 298 0
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2022-09-21 66332 101 298 0
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2022-09-08 65475 60 297 0
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2022-09-05 65299 88 297 1
2022-09-02 65211 46 296 0
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2022-08-10 64300 75 295 0
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2022-07-27 63174 192 295 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: 0:03:13.447186