Germany: LK Vorpommern-Rügen (Mecklenburg-Vorpommern)¶

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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 13:14:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Vorpommern-Rügen", weeks=5);
2023-03-07T13:14:55.978594 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 39.8 LK Vorpommern-Rügen, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 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 Vorpommern-Rügen new deaths (rolling 7d mean) Germany-LK Vorpommern-Rügen 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 Vorpommern-Rügen cases daily growth factor Germany-LK Vorpommern-Rügen cases daily growth factor (rolling mean) Germany-LK Vorpommern-Rügen 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 Vorpommern-Rügen deaths daily growth factor Germany-LK Vorpommern-Rügen deaths daily growth factor (rolling mean) Germany-LK Vorpommern-Rügen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Vorpommern-Rügen doubling time cases (rolling mean) 0.00 11.29 22.59 33.88 45.18 daily change Germany-LK Vorpommern-Rügen new cases (rolling 7d mean) Germany-LK Vorpommern-Rügen new cases 0.000 0.237 0.474 0.711 0.947
In [4]:
overview(country="Germany", subregion="LK Vorpommern-Rügen");
2023-03-07T13:15:15.068705 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) 39.8 LK Vorpommern-Rügen, 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 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 Vorpommern-Rügen cases daily growth factor Germany-LK Vorpommern-Rügen cases daily growth factor (rolling mean) Germany-LK Vorpommern-Rügen 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 Vorpommern-Rügen deaths daily growth factor Germany-LK Vorpommern-Rügen deaths daily growth factor (rolling mean) Germany-LK Vorpommern-Rügen 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 Vorpommern-Rügen doubling time cases (rolling mean) Germany-LK Vorpommern-Rügen doubling time deaths (rolling mean) 0 452 904 1355 daily change Germany-LK Vorpommern-Rügen new cases (rolling 7d mean) Germany-LK Vorpommern-Rügen new cases 0.000 2.259 4.518 6.777 daily change Germany-LK Vorpommern-Rügen new deaths (rolling 7d mean) Germany-LK Vorpommern-Rügen new deaths 0.0 78.0 155.9 233.9 311.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Vorpommern-Rügen", dates="2020-03-15:");
2023-03-07T13:17:56.535505 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 Vorpommern-Rügen LK Vorpommern-Rügen 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 Vorpommern-Rügen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Vorpommern-Rügen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Vorpommern-Rügen")
print(f'Population of country="Germany", subregion="LK Vorpommern-Rügen": {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 Vorpommern-Rügen": 225900 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 96752 11 324 0
2023-03-03 96741 6 324 0
2023-03-02 96735 17 324 0
2023-03-01 96718 27 324 0
2023-02-28 96691 29 324 0
2023-02-27 96662 32 324 0
2023-02-24 96630 17 324 0
2023-02-23 96613 18 324 0
2023-02-22 96595 28 324 0
2023-02-21 96567 47 324 0
2023-02-20 96520 35 324 0
2023-02-17 96485 22 324 0
2023-02-16 96463 13 324 0
2023-02-15 96450 30 324 0
2023-02-14 96420 30 324 0
2023-02-13 96390 31 324 0
2023-02-10 96359 29 324 0
2023-02-09 96330 24 324 0
2023-02-08 96306 28 324 0
2023-02-07 96278 34 324 0
2023-02-06 96244 40 324 0
2023-02-03 96204 32 324 0
2023-02-02 96172 34 324 0
2023-02-01 96138 26 324 0
2023-01-31 96112 43 324 0
2023-01-30 96069 42 324 0
2023-01-27 96027 23 324 0
2023-01-26 96004 16 324 0
2023-01-25 95988 24 324 0
2023-01-24 95964 52 324 0
2023-01-23 95912 31 324 0
2023-01-20 95881 26 324 0
2023-01-19 95855 26 324 0
2023-01-18 95829 34 324 0
2023-01-17 95795 60 324 0
2023-01-16 95735 42 324 0
2023-01-13 95693 26 324 0
2023-01-12 95667 36 324 0
2023-01-11 95631 50 324 0
2023-01-10 95581 73 324 0
2023-01-09 95508 64 324 1
2023-01-06 95444 41 323 0
2023-01-05 95403 56 323 0
2023-01-04 95347 63 323 0
2023-01-03 95284 125 323 0
2023-01-02 95159 79 323 0
2022-12-30 95080 66 323 0
2022-12-29 95014 85 323 0
2022-12-28 94929 118 323 0
2022-12-27 94811 159 323 0
2022-12-23 94652 110 323 0
2022-12-22 94542 112 323 0
2022-12-21 94430 115 323 0
2022-12-20 94315 217 323 0
2022-12-19 94098 163 323 0
2022-12-16 93935 99 323 0
2022-12-15 93836 152 323 0
2022-12-14 93684 164 323 1
2022-12-13 93520 209 322 0
2022-12-12 93311 220 322 0
2022-12-09 93091 61 322 0
2022-12-08 93030 120 322 0
2022-12-07 92910 131 322 0
2022-12-06 92779 238 322 0
2022-12-05 92541 168 322 0
2022-12-02 92373 106 322 0
2022-12-01 92267 92 322 0
2022-11-30 92175 105 322 0
2022-11-29 92070 240 322 0
2022-11-28 91830 117 322 0
2022-11-25 91713 67 322 0
2022-11-24 91646 91 322 0
2022-11-23 91555 94 322 1
2022-11-22 91461 171 321 0
2022-11-21 91290 104 321 0
2022-11-18 91186 60 321 0
2022-11-17 91126 99 321 0
2022-11-16 91027 72 321 0
2022-11-15 90955 148 321 0
2022-11-14 90807 152 321 0
2022-11-13 90655 2 321 0
2022-11-12 90653 1 321 0
2022-11-11 90652 81 321 0
2022-11-10 90571 127 321 0
2022-11-09 90444 138 321 0
2022-11-08 90306 223 321 0
2022-11-07 90083 156 321 0
2022-11-04 89927 126 321 0
2022-11-03 89801 175 321 0
2022-11-02 89626 281 321 0
2022-11-01 89345 298 321 0
2022-10-31 89047 4 321 0
2022-10-30 89043 1 321 0
2022-10-29 89042 3 321 0
2022-10-28 89039 193 321 0
2022-10-27 88846 216 321 0
2022-10-26 88630 221 321 0
2022-10-25 88409 424 321 1
2022-10-24 87985 304 320 0
2022-10-22 87681 3 320 0
2022-10-21 87678 223 320 0
2022-10-20 87455 273 320 0
2022-10-19 87182 317 320 0
2022-10-18 86865 518 320 0
2022-10-17 86347 360 320 1
2022-10-15 85987 16 319 0
2022-10-14 85971 235 319 1
2022-10-13 85736 305 318 0
2022-10-12 85431 403 318 0
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2021-01-01 1265 7 31 0
2020-12-31 1258 19 31 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: 0:03:46.858315