Germany: SK Schwerin (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 12:59:19 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Schwerin", weeks=5);
2023-03-07T12:59:29.756672 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 60 60 80 80 100 100 120 120 7-day incidence rate (per 100K people) 57.4 SK Schwerin, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 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-SK Schwerin cases daily growth factor Germany-SK Schwerin cases daily growth factor (rolling mean) Germany-SK Schwerin 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-SK Schwerin deaths daily growth factor Germany-SK Schwerin deaths daily growth factor (rolling mean) Germany-SK Schwerin 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-SK Schwerin doubling time cases (rolling mean) 0.00 9.57 19.15 28.72 daily change Germany-SK Schwerin new cases (rolling 7d mean) Germany-SK Schwerin new cases 0.000 0.239 0.479 0.718 0.957 daily change Germany-SK Schwerin new deaths (rolling 7d mean) Germany-SK Schwerin new deaths 0.000 0.286 0.572 0.858
In [4]:
overview(country="Germany", subregion="SK Schwerin");
2023-03-07T12:59:46.276559 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) 57.4 SK Schwerin, 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 0.0 2.5 5.0 7.5 10.0 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-SK Schwerin cases daily growth factor Germany-SK Schwerin cases daily growth factor (rolling mean) Germany-SK Schwerin 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-SK Schwerin deaths daily growth factor Germany-SK Schwerin deaths daily growth factor (rolling mean) Germany-SK Schwerin 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 cases doubling time [days] Germany-SK Schwerin doubling time cases (rolling mean) Germany-SK Schwerin doubling time deaths (rolling mean) 0.0 191.5 383.0 daily change Germany-SK Schwerin new cases (rolling 7d mean) Germany-SK Schwerin new cases 0.00 2.39 4.79 7.18 9.57 daily change Germany-SK Schwerin new deaths (rolling 7d mean) Germany-SK Schwerin new deaths 0.0 55.2 110.4 165.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Schwerin", dates="2020-03-15:");
2023-03-07T13:02:01.973571 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: SK Schwerin SK Schwerin 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 SK Schwerin Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Schwerin")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Schwerin")
print(f'Population of country="Germany", subregion="SK Schwerin": {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="SK Schwerin": 95740 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 44608 2 215 0
2023-03-03 44606 2 215 0
2023-03-02 44604 4 215 0
2023-03-01 44600 12 215 0
2023-02-28 44588 35 215 0
2023-02-27 44553 12 215 0
2023-02-24 44541 16 215 0
2023-02-23 44525 16 215 0
2023-02-22 44509 13 215 0
2023-02-21 44496 15 215 0
2023-02-20 44481 19 215 0
2023-02-19 44462 5 215 0
2023-02-18 44457 6 215 0
2023-02-17 44451 3 215 0
2023-02-16 44448 8 215 0
2023-02-15 44440 16 215 0
2023-02-14 44424 10 215 1
2023-02-13 44414 22 214 0
2023-02-12 44392 2 214 0
2023-02-11 44390 1 214 0
2023-02-10 44389 8 214 0
2023-02-09 44381 10 214 0
2023-02-08 44371 18 214 0
2023-02-07 44353 23 214 0
2023-02-06 44330 22 214 0
2023-02-04 44308 2 214 0
2023-02-03 44306 17 214 0
2023-02-02 44289 24 214 0
2023-02-01 44265 22 214 0
2023-01-31 44243 30 214 0
2023-01-30 44213 25 214 0
2023-01-27 44188 10 214 0
2023-01-26 44178 12 214 0
2023-01-25 44166 19 214 0
2023-01-24 44147 28 214 0
2023-01-23 44119 4 214 0
2023-01-22 44115 3 214 0
2023-01-20 44112 12 214 0
2023-01-19 44100 6 214 0
2023-01-18 44094 9 214 0
2023-01-17 44085 28 214 0
2023-01-16 44057 18 214 0
2023-01-14 44039 2 214 0
2023-01-13 44037 17 214 0
2023-01-12 44020 15 214 0
2023-01-11 44005 20 214 0
2023-01-10 43985 21 214 0
2023-01-09 43964 72 214 0
2023-01-08 43892 4 214 0
2023-01-07 43888 6 214 0
2023-01-06 43882 27 214 1
2023-01-05 43855 40 213 1
2023-01-04 43815 49 212 0
2023-01-03 43766 76 212 0
2023-01-02 43690 88 212 1
2023-01-01 43602 4 211 0
2022-12-31 43598 3 211 0
2022-12-30 43595 49 211 1
2022-12-29 43546 41 210 0
2022-12-28 43505 150 210 0
2022-12-27 43355 122 210 1
2022-12-25 43233 1 209 0
2022-12-24 43232 2 209 0
2022-12-23 43230 79 209 0
2022-12-22 43151 86 209 0
2022-12-21 43065 77 209 0
2022-12-20 42988 170 209 2
2022-12-19 42818 157 207 2
2022-12-16 42661 42 205 0
2022-12-15 42619 59 205 0
2022-12-14 42560 89 205 0
2022-12-13 42471 148 205 2
2022-12-12 42323 150 203 1
2022-12-11 42173 8 202 0
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2022-11-16 40673 64 196 0
2022-11-15 40609 109 196 0
2022-11-14 40500 27 196 0
2022-11-13 40473 2 196 0
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2022-11-06 40194 1 196 0
2022-11-05 40193 14 196 0
2022-11-04 40179 40 196 0
2022-11-03 40139 51 196 0
2022-11-02 40088 110 196 0
2022-11-01 39978 139 196 1
2022-10-31 39839 7 195 0
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2021-02-08 1467 5 81 3
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2021-02-05 1449 22 77 3
2021-02-04 1427 12 74 1
2021-02-03 1415 26 73 3
2021-02-02 1389 28 70 3
2021-02-01 1361 5 67 1
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2021-01-30 1354 14 65 0
2021-01-29 1340 10 65 1
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2021-01-27 1325 11 64 1
2021-01-26 1314 32 63 7
2021-01-25 1282 5 56 0
2021-01-23 1277 9 56 0
2021-01-22 1268 13 56 0
2021-01-21 1255 13 56 0
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2021-01-05 996 35 40 2
2021-01-04 961 8 38 0
2021-01-02 953 2 38 0
2021-01-01 951 4 38 0
2020-12-31 947 35 38 4
2020-12-30 912 77 34 10
2020-12-29 835 21 24 0
2020-12-28 814 3 24 1
2020-12-27 811 1 23 0
2020-12-26 810 6 23 2
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2020-12-23 790 34 20 2
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2020-12-17 685 55 15 4
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2020-11-02 210 3 2 0
2020-10-31 207 16 2 0
2020-10-30 191 8 2 0
2020-10-29 183 9 2 0
2020-10-28 174 5 2 0
2020-10-27 169 12 2 0
2020-10-26 157 1 2 1
2020-10-25 156 1 1 0
2020-10-24 155 3 1 0
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2020-07-28 111 1 0 0
2020-07-24 110 7 0 0
2020-07-23 103 9 0 0
2020-07-22 94 2 0 0
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2020-04-04 76 1 0 0
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2020-03-31 70 8 0 0
2020-03-29 62 2 0 0
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2020-03-19 29 10 0 0
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2020-03-12 2 1 0 0

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: 0:03:32.843356