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

  • Homepage of project: https://oscovida.github.io
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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 12:59:00 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Vorpommern-Greifswald", weeks=5);
2023-03-07T12:59:14.012401 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 80 80 7-day incidence rate (per 100K people) 62.9 LK Vorpommern-Greifswald, 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 0.0 0.1 0.2 0.3 0.4 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Vorpommern-Greifswald cases daily growth factor Germany-LK Vorpommern-Greifswald cases daily growth factor (rolling mean) Germany-LK Vorpommern-Greifswald estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Vorpommern-Greifswald deaths daily growth factor Germany-LK Vorpommern-Greifswald deaths daily growth factor (rolling mean) Germany-LK Vorpommern-Greifswald estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Vorpommern-Greifswald doubling time cases (rolling mean) 0.00 11.77 23.55 35.32 47.09 daily change Germany-LK Vorpommern-Greifswald new cases (rolling 7d mean) Germany-LK Vorpommern-Greifswald new cases 0.000 0.235 0.471 0.706 0.942 daily change Germany-LK Vorpommern-Greifswald new deaths (rolling 7d mean) Germany-LK Vorpommern-Greifswald new deaths 0.000 0.210 0.419 0.629 0.839
In [4]:
overview(country="Germany", subregion="LK Vorpommern-Greifswald");
2023-03-07T12:59:32.398691 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) 62.9 LK Vorpommern-Greifswald, 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 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Vorpommern-Greifswald cases daily growth factor Germany-LK Vorpommern-Greifswald cases daily growth factor (rolling mean) Germany-LK Vorpommern-Greifswald estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Vorpommern-Greifswald deaths daily growth factor Germany-LK Vorpommern-Greifswald deaths daily growth factor (rolling mean) Germany-LK Vorpommern-Greifswald 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 Vorpommern-Greifswald doubling time cases (rolling mean) Germany-LK Vorpommern-Greifswald doubling time deaths (rolling mean) 0 471 942 1413 daily change Germany-LK Vorpommern-Greifswald new cases (rolling 7d mean) Germany-LK Vorpommern-Greifswald new cases 0.00 2.35 4.71 7.06 9.42 daily change Germany-LK Vorpommern-Greifswald new deaths (rolling 7d mean) Germany-LK Vorpommern-Greifswald new deaths 0.0 153.1 306.2 459.2 612.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Vorpommern-Greifswald", dates="2020-03-15:");
2023-03-07T13:02:03.586653 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-Greifswald LK Vorpommern-Greifswald 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Vorpommern-Greifswald Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Vorpommern-Greifswald")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Vorpommern-Greifswald")
print(f'Population of country="Germany", subregion="LK Vorpommern-Greifswald": {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-Greifswald": 235451 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 106563 25 583 0
2023-03-03 106538 19 583 0
2023-03-02 106519 16 583 0
2023-03-01 106503 49 583 0
2023-02-28 106454 39 583 0
2023-02-27 106415 49 583 1
2023-02-24 106366 30 582 0
2023-02-23 106336 16 582 0
2023-02-22 106320 16 582 0
2023-02-21 106304 43 582 0
2023-02-20 106261 23 582 0
2023-02-17 106238 22 582 0
2023-02-16 106216 20 582 0
2023-02-15 106196 22 582 0
2023-02-14 106174 35 582 0
2023-02-13 106139 34 582 0
2023-02-10 106105 30 582 0
2023-02-09 106075 22 582 0
2023-02-08 106053 43 582 0
2023-02-07 106010 19 582 0
2023-02-06 105991 31 582 0
2023-02-03 105960 28 582 0
2023-02-02 105932 29 582 0
2023-02-01 105903 32 582 0
2023-01-31 105871 39 582 0
2023-01-30 105832 48 582 0
2023-01-27 105784 39 582 0
2023-01-26 105745 24 582 0
2023-01-25 105721 38 582 0
2023-01-24 105683 62 582 0
2023-01-23 105621 26 582 1
2023-01-20 105595 37 581 0
2023-01-19 105558 23 581 1
2023-01-18 105535 31 580 0
2023-01-17 105504 51 580 0
2023-01-16 105453 37 580 0
2023-01-13 105416 30 580 1
2023-01-12 105386 16 579 0
2023-01-11 105370 41 579 0
2023-01-10 105329 71 579 0
2023-01-09 105258 70 579 0
2023-01-06 105188 43 579 0
2023-01-05 105145 38 579 0
2023-01-04 105107 90 579 0
2023-01-03 105017 136 579 0
2023-01-02 104881 96 579 0
2022-12-30 104785 61 579 1
2022-12-29 104724 107 578 1
2022-12-28 104617 134 577 0
2022-12-27 104483 194 577 0
2022-12-23 104289 120 577 0
2022-12-22 104169 172 577 3
2022-12-21 103997 179 574 0
2022-12-20 103818 217 574 0
2022-12-19 103601 205 574 1
2022-12-16 103396 128 573 0
2022-12-15 103268 162 573 0
2022-12-14 103106 247 573 0
2022-12-13 102859 187 573 1
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2022-12-09 102441 123 571 1
2022-12-08 102318 156 570 0
2022-12-07 102162 144 570 0
2022-12-06 102018 241 570 2
2022-12-05 101777 229 568 2
2022-12-02 101548 143 566 0
2022-12-01 101405 113 566 0
2022-11-30 101292 166 566 0
2022-11-29 101126 228 566 0
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2022-11-09 99204 159 564 1
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2022-11-04 98652 142 561 0
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2022-11-01 97994 299 560 2
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2022-10-25 97024 318 558 0
2022-10-24 96706 285 558 1
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2022-10-13 94594 257 557 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: 0:03:52.905780