Germany: SK Schweinfurt (Bayern)¶

  • 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 11:58:06 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Schweinfurt", weeks=5);
2023-03-07T11:58:18.805846 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 69.0 SK Schweinfurt, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 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 Schweinfurt cases daily growth factor Germany-SK Schweinfurt cases daily growth factor (rolling mean) Germany-SK Schweinfurt estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Schweinfurt deaths daily growth factor Germany-SK Schweinfurt deaths daily growth factor (rolling mean) Germany-SK Schweinfurt estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK Schweinfurt doubling time cases (rolling mean) 0.00 10.72 21.43 32.15 daily change Germany-SK Schweinfurt new cases (rolling 7d mean) Germany-SK Schweinfurt new cases 0.000 0.268 0.536 0.804 daily change Germany-SK Schweinfurt new deaths (rolling 7d mean) Germany-SK Schweinfurt new deaths 0.000 0.348 0.695
In [4]:
overview(country="Germany", subregion="SK Schweinfurt");
2023-03-07T11:58:41.341983 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) 69.0 SK Schweinfurt, 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 0.0 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 Schweinfurt cases daily growth factor Germany-SK Schweinfurt cases daily growth factor (rolling mean) Germany-SK Schweinfurt estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-SK Schweinfurt deaths daily growth factor Germany-SK Schweinfurt deaths daily growth factor (rolling mean) Germany-SK Schweinfurt 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-SK Schweinfurt doubling time cases (rolling mean) Germany-SK Schweinfurt doubling time deaths (rolling mean) 0.0 107.2 214.3 321.5 daily change Germany-SK Schweinfurt new cases (rolling 7d mean) Germany-SK Schweinfurt new cases 0.000 1.340 2.679 4.019 5.359 daily change Germany-SK Schweinfurt new deaths (rolling 7d mean) Germany-SK Schweinfurt new deaths 0.0 30.9 61.7 92.6 123.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Schweinfurt", dates="2020-03-15:");
2023-03-07T12:01:55.153459 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: SK Schweinfurt SK Schweinfurt 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 Schweinfurt Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Schweinfurt")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Schweinfurt")
print(f'Population of country="Germany", subregion="SK Schweinfurt": {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 Schweinfurt": 53585 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-05 28902 6 203 0
2023-03-04 28896 3 203 0
2023-03-03 28893 5 203 0
2023-03-02 28888 6 203 0
2023-03-01 28882 6 203 0
2023-02-28 28876 11 203 0
2023-02-27 28865 38 203 0
2023-02-26 28827 4 203 0
2023-02-25 28823 9 203 0
2023-02-24 28814 12 203 0
2023-02-23 28802 10 203 0
2023-02-22 28792 10 203 0
2023-02-21 28782 22 203 0
2023-02-20 28760 15 203 1
2023-02-18 28745 12 202 0
2023-02-17 28733 11 202 1
2023-02-16 28722 13 201 0
2023-02-15 28709 12 201 0
2023-02-14 28697 13 201 0
2023-02-13 28684 24 201 1
2023-02-12 28660 4 200 0
2023-02-11 28656 1 200 0
2023-02-10 28655 9 200 0
2023-02-09 28646 6 200 1
2023-02-08 28640 15 199 0
2023-02-07 28625 10 199 0
2023-02-06 28615 12 199 0
2023-02-05 28603 1 199 0
2023-02-04 28602 6 199 1
2023-02-03 28596 3 198 0
2023-02-02 28593 9 198 1
2023-02-01 28584 7 197 0
2023-01-31 28577 10 197 0
2023-01-30 28567 10 197 0
2023-01-29 28557 2 197 0
2023-01-28 28555 7 197 0
2023-01-27 28548 11 197 0
2023-01-26 28537 14 197 0
2023-01-25 28523 9 197 0
2023-01-24 28514 7 197 1
2023-01-23 28507 8 196 0
2023-01-22 28499 2 196 0
2023-01-21 28497 7 196 0
2023-01-20 28490 2 196 0
2023-01-19 28488 3 196 0
2023-01-18 28485 7 196 0
2023-01-17 28478 2 196 0
2023-01-16 28476 3 196 0
2023-01-15 28473 2 196 0
2023-01-14 28471 2 196 0
2023-01-12 28469 5 196 0
2023-01-11 28464 6 196 0
2023-01-10 28458 7 196 0
2023-01-09 28451 2 196 0
2023-01-07 28449 6 196 0
2023-01-06 28443 6 196 0
2023-01-05 28437 5 196 0
2023-01-04 28432 5 196 0
2023-01-03 28427 14 196 0
2023-01-02 28413 14 196 0
2022-12-31 28399 6 196 0
2022-12-30 28393 10 196 0
2022-12-29 28383 19 196 0
2022-12-28 28364 11 196 0
2022-12-27 28353 18 196 0
2022-12-26 28335 2 196 0
2022-12-25 28333 2 196 1
2022-12-24 28331 10 195 0
2022-12-23 28321 16 195 0
2022-12-22 28305 33 195 0
2022-12-21 28272 31 195 0
2022-12-20 28241 26 195 0
2022-12-19 28215 22 195 2
2022-12-18 28193 1 193 0
2022-12-17 28192 10 193 0
2022-12-16 28182 13 193 0
2022-12-15 28169 10 193 1
2022-12-14 28159 18 192 0
2022-12-13 28141 12 192 0
2022-12-12 28129 12 192 0
2022-12-11 28117 3 192 0
2022-12-10 28114 7 192 0
2022-12-09 28107 7 192 0
2022-12-08 28100 10 192 0
2022-12-07 28090 8 192 0
2022-12-06 28082 15 192 0
2022-12-05 28067 6 192 0
2022-12-04 28061 1 192 0
2022-12-03 28060 8 192 0
2022-12-02 28052 4 192 0
2022-12-01 28048 14 192 0
2022-11-30 28034 13 192 1
2022-11-29 28021 15 191 0
2022-11-28 28006 7 191 0
2022-11-27 27999 3 191 0
2022-11-26 27996 6 191 1
2022-11-25 27990 10 190 1
2022-11-24 27980 10 189 0
2022-11-23 27970 6 189 0
2022-11-22 27964 5 189 0
2022-11-21 27959 13 189 0
2022-11-20 27946 3 189 0
2022-11-19 27943 3 189 0
2022-11-18 27940 12 189 0
2022-11-17 27928 14 189 0
2022-11-16 27914 14 189 0
2022-11-15 27900 12 189 0
2022-11-14 27888 17 189 0
2022-11-13 27871 4 189 0
2022-11-12 27867 11 189 0
2022-11-11 27856 15 189 0
2022-11-10 27841 17 189 0
2022-11-09 27824 17 189 0
2022-11-08 27807 25 189 0
2022-11-07 27782 26 189 0
2022-11-06 27756 3 189 0
2022-11-05 27753 7 189 0
2022-11-04 27746 20 189 1
2022-11-03 27726 32 188 0
2022-11-02 27694 36 188 1
2022-11-01 27658 22 187 0
2022-10-31 27636 25 187 0
2022-10-30 27611 7 187 0
2022-10-29 27604 9 187 1
2022-10-28 27595 24 186 0
2022-10-27 27571 82 186 0
2022-10-26 27489 22 186 0
2022-10-25 27467 49 186 1
2022-10-24 27418 39 185 0
2022-10-23 27379 14 185 1
2022-10-22 27365 30 184 1
2022-10-21 27335 57 183 0
2022-10-20 27278 36 183 1
2022-10-19 27242 55 182 0
2022-10-18 27187 76 182 0
2022-10-17 27111 68 182 1
2022-10-16 27043 25 181 0
2022-10-15 27018 41 181 0
2022-10-14 26977 66 181 1
2022-10-13 26911 71 180 0
2022-10-12 26840 90 180 0
2022-10-11 26750 95 180 4
2022-10-10 26655 70 176 1
2022-10-09 26585 14 175 0
2022-10-08 26571 51 175 0
2022-10-07 26520 47 175 1
2022-10-06 26473 126 174 0
2022-10-05 26347 82 174 0
2022-10-04 26265 114 174 0
2022-10-03 26151 9 174 0
2022-10-02 26142 12 174 0
2022-10-01 26130 43 174 0
2022-09-30 26087 57 174 0
2022-09-29 26030 19 174 0
2022-09-28 26011 76 174 1
2022-09-27 25935 63 173 0
2022-09-26 25872 29 173 1
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2020-12-29 1279 26 42 2
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2020-12-27 1242 1 40 0
2020-12-26 1241 1 40 0
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2020-12-15 1132 16 36 0
2020-12-14 1116 5 36 0
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2020-12-11 1096 17 36 1
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2020-12-07 1033 4 33 0
2020-12-06 1029 1 33 0
2020-12-05 1028 22 33 0
2020-12-04 1006 9 33 0
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2020-08-27 224 1 17 0
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2020-06-20 198 1 17 0
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2020-05-05 190 1 17 0
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2020-04-04 84 8 14 1
2020-04-03 76 6 13 2
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2020-04-01 63 6 11 0
2020-03-31 57 1 11 1
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2020-03-28 54 26 10 6
2020-03-27 28 9 4 1
2020-03-26 19 4 3 2
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2020-03-23 11 4 1 1
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2020-03-19 4 2 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: 0:04:08.985782