Germany: LK Deggendorf (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 10:33:39 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Deggendorf", weeks=5);
2023-03-07T10:33:52.244138 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 150 150 7-day incidence rate (per 100K people) 54.8 LK Deggendorf, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Deggendorf cases daily growth factor Germany-LK Deggendorf cases daily growth factor (rolling mean) Germany-LK Deggendorf 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 Deggendorf deaths daily growth factor Germany-LK Deggendorf deaths daily growth factor (rolling mean) Germany-LK Deggendorf 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 Deggendorf doubling time cases (rolling mean) 0.00 12.05 24.10 36.16 48.21 daily change Germany-LK Deggendorf new cases (rolling 7d mean) Germany-LK Deggendorf new cases 0.000 0.241 0.482 0.723 0.964 daily change Germany-LK Deggendorf new deaths (rolling 7d mean) Germany-LK Deggendorf new deaths 0.000 0.279 0.559 0.838
In [4]:
overview(country="Germany", subregion="LK Deggendorf");
2023-03-07T10:34:09.978762 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) 54.8 LK Deggendorf, 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 Deggendorf cases daily growth factor Germany-LK Deggendorf cases daily growth factor (rolling mean) Germany-LK Deggendorf 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 Deggendorf deaths daily growth factor Germany-LK Deggendorf deaths daily growth factor (rolling mean) Germany-LK Deggendorf 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 Deggendorf doubling time cases (rolling mean) Germany-LK Deggendorf doubling time deaths (rolling mean) 0.0 241.0 482.1 daily change Germany-LK Deggendorf new cases (rolling 7d mean) Germany-LK Deggendorf new cases 0.00 2.41 4.82 7.23 9.64 daily change Germany-LK Deggendorf new deaths (rolling 7d mean) Germany-LK Deggendorf new deaths 0.0 73.5 147.0 220.4 293.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Deggendorf", dates="2020-03-15:");
2023-03-07T10:36:59.002147 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 Deggendorf LK Deggendorf 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 Deggendorf Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Deggendorf")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Deggendorf")
print(f'Population of country="Germany", subregion="LK Deggendorf": {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 Deggendorf": 120521 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 61260 2 327 0
2023-03-04 61258 2 327 0
2023-03-03 61256 9 327 0
2023-03-02 61247 10 327 0
2023-03-01 61237 16 327 0
2023-02-28 61221 27 327 0
2023-02-27 61194 6 327 0
2023-02-26 61188 7 327 0
2023-02-25 61181 7 327 0
2023-02-24 61174 23 327 1
2023-02-23 61151 16 326 0
2023-02-22 61135 20 326 0
2023-02-21 61115 21 326 0
2023-02-20 61094 7 326 0
2023-02-19 61087 5 326 1
2023-02-18 61082 7 325 0
2023-02-17 61075 41 325 0
2023-02-16 61034 21 325 0
2023-02-15 61013 25 325 0
2023-02-14 60988 32 325 0
2023-02-13 60956 3 325 0
2023-02-12 60953 8 325 0
2023-02-11 60945 18 325 0
2023-02-10 60927 37 325 0
2023-02-09 60890 21 325 0
2023-02-08 60869 18 325 0
2023-02-07 60851 40 325 1
2023-02-06 60811 10 324 0
2023-02-05 60801 8 324 0
2023-02-04 60793 13 324 0
2023-02-03 60780 42 324 0
2023-02-02 60738 24 324 0
2023-02-01 60714 27 324 0
2023-01-31 60687 52 324 0
2023-01-30 60635 20 324 0
2023-01-29 60615 10 324 0
2023-01-28 60605 13 324 0
2023-01-27 60592 32 324 0
2023-01-26 60560 35 324 0
2023-01-25 60525 21 324 0
2023-01-24 60504 37 324 0
2023-01-23 60467 14 324 0
2023-01-22 60453 4 324 0
2023-01-21 60449 5 324 0
2023-01-20 60444 18 324 0
2023-01-19 60426 21 324 0
2023-01-18 60405 21 324 0
2023-01-17 60384 20 324 0
2023-01-16 60364 21 324 0
2023-01-15 60343 1 324 0
2023-01-14 60342 5 324 1
2023-01-13 60337 21 323 0
2023-01-12 60316 21 323 0
2023-01-11 60295 19 323 0
2023-01-10 60276 37 323 0
2023-01-09 60239 16 323 0
2023-01-08 60223 8 323 0
2023-01-07 60215 4 323 0
2023-01-06 60211 14 323 0
2023-01-05 60197 39 323 0
2023-01-04 60158 22 323 0
2023-01-03 60136 19 323 0
2023-01-02 60117 2 323 0
2023-01-01 60115 3 323 1
2022-12-31 60112 14 322 0
2022-12-30 60098 26 322 0
2022-12-29 60072 21 322 1
2022-12-28 60051 27 321 0
2022-12-27 60024 7 321 1
2022-12-26 60017 14 320 0
2022-12-25 60003 2 320 0
2022-12-23 60001 16 320 0
2022-12-22 59985 15 320 0
2022-12-21 59970 28 320 1
2022-12-20 59942 29 319 1
2022-12-19 59913 19 318 0
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2022-12-17 59885 7 318 0
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2022-12-15 59856 23 318 0
2022-12-14 59833 21 318 1
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2022-12-07 59700 15 316 0
2022-12-06 59685 34 316 0
2022-12-05 59651 6 316 0
2022-12-04 59645 4 316 0
2022-12-03 59641 7 316 0
2022-12-02 59634 25 316 0
2022-12-01 59609 12 316 0
2022-11-30 59597 23 316 0
2022-11-29 59574 29 316 0
2022-11-28 59545 7 316 0
2022-11-27 59538 7 316 0
2022-11-26 59531 10 316 0
2022-11-25 59521 16 316 0
2022-11-24 59505 15 316 0
2022-11-23 59490 19 316 0
2022-11-22 59471 21 316 0
2022-11-21 59450 4 316 0
2022-11-20 59446 4 316 0
2022-11-19 59442 6 316 0
2022-11-18 59436 18 316 0
2022-11-17 59418 24 316 0
2022-11-16 59394 31 316 1
2022-11-15 59363 22 315 0
2022-11-14 59341 28 315 0
2022-11-13 59313 7 315 1
2022-11-12 59306 11 314 0
2022-11-11 59295 39 314 0
2022-11-10 59256 22 314 0
2022-11-09 59234 40 314 1
2022-11-08 59194 43 313 1
2022-11-07 59151 18 312 0
2022-11-06 59133 9 312 0
2022-11-05 59124 25 312 0
2022-11-04 59099 58 312 0
2022-11-03 59041 47 312 0
2022-11-02 58994 92 312 0
2022-10-31 58902 20 312 0
2022-10-30 58882 13 312 0
2022-10-29 58869 32 312 0
2022-10-28 58837 59 312 0
2022-10-27 58778 43 312 0
2022-10-26 58735 76 312 0
2022-10-25 58659 102 312 0
2022-10-24 58557 51 312 0
2022-10-23 58506 27 312 0
2022-10-22 58479 39 312 0
2022-10-21 58440 86 312 0
2022-10-20 58354 69 312 1
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2022-10-18 58189 175 311 0
2022-10-17 58014 37 311 0
2022-10-16 57977 91 311 0
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2022-10-11 57277 262 311 1
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2022-10-09 56937 55 310 0
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2022-10-07 56774 326 310 1
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2022-10-05 56246 285 308 4
2022-10-04 55961 82 304 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: 0:03:34.221117