Germany: LK Rostock (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 12:51:58 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rostock", weeks=5);
2023-03-07T12:52:09.300565 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 7-day incidence rate (per 100K people) 43.6 LK Rostock, 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 R & growth factor (based on cases) Germany-LK Rostock cases daily growth factor Germany-LK Rostock cases daily growth factor (rolling mean) Germany-LK Rostock estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 5 5 R & growth factor (based on deaths) Germany-LK Rostock deaths daily growth factor Germany-LK Rostock deaths daily growth factor (rolling mean) Germany-LK Rostock 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 Rostock doubling time cases (rolling mean) 0.00 10.89 21.78 32.67 43.56 daily change Germany-LK Rostock new cases (rolling 7d mean) Germany-LK Rostock new cases 0.000 0.218 0.436 0.653 0.871 daily change Germany-LK Rostock new deaths (rolling 7d mean) Germany-LK Rostock new deaths 0.000 0.238 0.477 0.715 0.953
In [4]:
overview(country="Germany", subregion="LK Rostock");
2023-03-07T12:52:27.917304 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) 43.6 LK Rostock, 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 Rostock cases daily growth factor Germany-LK Rostock cases daily growth factor (rolling mean) Germany-LK Rostock 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 5 5 R & growth factor (based on deaths) Germany-LK Rostock deaths daily growth factor Germany-LK Rostock deaths daily growth factor (rolling mean) Germany-LK Rostock 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 Rostock doubling time cases (rolling mean) Germany-LK Rostock doubling time deaths (rolling mean) 0 436 871 1307 daily change Germany-LK Rostock new cases (rolling 7d mean) Germany-LK Rostock new cases 0.000 2.178 4.356 6.534 daily change Germany-LK Rostock new deaths (rolling 7d mean) Germany-LK Rostock new deaths 0.0 88.1 176.3 264.4 352.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rostock", dates="2020-03-15:");
2023-03-07T12:55:06.621538 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 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 Rostock LK Rostock 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 Rostock Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rostock")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rostock")
print(f'Population of country="Germany", subregion="LK Rostock": {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 Rostock": 217796 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 99308 11 372 0
2023-03-03 99297 8 372 0
2023-03-02 99289 24 372 0
2023-03-01 99265 20 372 0
2023-02-28 99245 32 372 0
2023-02-27 99213 13 372 1
2023-02-24 99200 17 371 0
2023-02-23 99183 15 371 1
2023-02-22 99168 15 370 0
2023-02-21 99153 42 370 0
2023-02-20 99111 13 370 0
2023-02-17 99098 16 370 0
2023-02-16 99082 17 370 0
2023-02-15 99065 30 370 0
2023-02-14 99035 32 370 0
2023-02-13 99003 18 370 1
2023-02-10 98985 20 369 0
2023-02-09 98965 8 369 0
2023-02-08 98957 10 369 0
2023-02-07 98947 36 369 0
2023-02-06 98911 29 369 0
2023-02-03 98882 33 369 0
2023-02-02 98849 20 369 0
2023-02-01 98829 29 369 0
2023-01-31 98800 51 369 0
2023-01-30 98749 25 369 0
2023-01-27 98724 28 369 0
2023-01-26 98696 30 369 0
2023-01-25 98666 29 369 1
2023-01-24 98637 63 368 0
2023-01-23 98574 35 368 0
2023-01-21 98539 1 368 0
2023-01-20 98538 25 368 0
2023-01-19 98513 24 368 0
2023-01-18 98489 28 368 0
2023-01-17 98461 64 368 0
2023-01-16 98397 32 368 0
2023-01-13 98365 29 368 0
2023-01-12 98336 25 368 0
2023-01-11 98311 29 368 0
2023-01-10 98282 72 368 0
2023-01-09 98210 60 368 1
2023-01-06 98150 33 367 0
2023-01-05 98117 43 367 1
2023-01-04 98074 85 366 1
2023-01-03 97989 118 365 0
2023-01-02 97871 70 365 2
2023-01-01 97801 3 363 0
2022-12-30 97798 70 363 0
2022-12-29 97728 78 363 0
2022-12-28 97650 127 363 0
2022-12-27 97523 109 363 0
2022-12-26 97414 2 363 0
2022-12-24 97412 1 363 0
2022-12-23 97411 113 363 1
2022-12-22 97298 97 362 0
2022-12-21 97201 125 362 0
2022-12-20 97076 182 362 0
2022-12-19 96894 79 362 1
2022-12-16 96815 96 361 1
2022-12-15 96719 79 360 1
2022-12-14 96640 120 359 0
2022-12-13 96520 165 359 0
2022-12-12 96355 107 359 0
2022-12-09 96248 141 359 0
2022-12-08 96107 81 359 1
2022-12-07 96026 114 358 0
2022-12-06 95912 234 358 1
2022-12-05 95678 136 357 0
2022-12-04 95542 1 357 0
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2022-11-08 93863 168 355 1
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2022-11-04 93589 114 354 1
2022-11-03 93475 168 353 0
2022-11-02 93307 278 353 1
2022-11-01 93029 217 352 0
2022-10-29 92812 1 352 0
2022-10-28 92811 137 352 0
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2022-10-24 92019 189 352 1
2022-10-22 91830 1 351 0
2022-10-21 91829 220 351 0
2022-10-20 91609 195 351 0
2022-10-19 91414 303 351 0
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2021-01-04 1056 21 31 2
2021-01-03 1035 1 29 0
2021-01-02 1034 8 29 2
2021-01-01 1026 7 27 1
2020-12-31 1019 26 26 3
2020-12-30 993 12 23 1
2020-12-29 981 27 22 0
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2020-12-22 893 64 22 7
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2020-10-12 118 2 3 0
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2020-04-01 34 1 2 1
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2020-03-17 12 1 0 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: 0:03:32.107248