Germany: SK Berlin Mitte (Berlin)¶

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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 12:06:50 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Mitte", weeks=5);
2023-03-07T12:07:07.667845 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) 84.2 SK Berlin Mitte, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Berlin Mitte new deaths (rolling 7d mean) Germany-SK Berlin Mitte 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-SK Berlin Mitte cases daily growth factor Germany-SK Berlin Mitte cases daily growth factor (rolling mean) Germany-SK Berlin Mitte 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-SK Berlin Mitte deaths daily growth factor Germany-SK Berlin Mitte deaths daily growth factor (rolling mean) Germany-SK Berlin Mitte 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-SK Berlin Mitte doubling time cases (rolling mean) 0.0 37.8 75.5 113.3 daily change Germany-SK Berlin Mitte new cases (rolling 7d mean) Germany-SK Berlin Mitte new cases 0.000 0.218 0.437 0.655 0.874
In [4]:
overview(country="Germany", subregion="SK Berlin Mitte");
2023-03-07T12:07:27.610084 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) 84.2 SK Berlin Mitte, 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 0 200 400 600 800 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 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 Berlin Mitte cases daily growth factor Germany-SK Berlin Mitte cases daily growth factor (rolling mean) Germany-SK Berlin Mitte 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-SK Berlin Mitte deaths daily growth factor Germany-SK Berlin Mitte deaths daily growth factor (rolling mean) Germany-SK Berlin Mitte 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 cases doubling time [days] Germany-SK Berlin Mitte doubling time cases (rolling mean) Germany-SK Berlin Mitte doubling time deaths (rolling mean) 0 755 1511 2266 3022 daily change Germany-SK Berlin Mitte new cases (rolling 7d mean) Germany-SK Berlin Mitte new cases 0.00 3.78 7.55 daily change Germany-SK Berlin Mitte new deaths (rolling 7d mean) Germany-SK Berlin Mitte new deaths 0.0 152.5 305.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Mitte", dates="2020-03-15:");
2023-03-07T12:09:35.172794 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 Berlin Mitte SK Berlin Mitte 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 Berlin Mitte Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Mitte")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Mitte")
print(f'Population of country="Germany", subregion="SK Berlin Mitte": {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 Berlin Mitte": 377735 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 158798 50 431 0
2023-03-03 158748 25 431 0
2023-03-02 158723 71 431 0
2023-03-01 158652 68 431 0
2023-02-28 158584 104 431 0
2023-02-27 158480 97 431 0
2023-02-24 158383 77 431 0
2023-02-23 158306 102 431 0
2023-02-22 158204 56 431 0
2023-02-21 158148 80 431 0
2023-02-20 158068 103 431 0
2023-02-17 157965 63 431 0
2023-02-16 157902 72 431 0
2023-02-15 157830 95 431 0
2023-02-14 157735 41 431 0
2023-02-13 157694 128 431 0
2023-02-10 157566 59 431 0
2023-02-09 157507 43 431 0
2023-02-08 157464 68 431 0
2023-02-07 157396 81 431 0
2023-02-06 157315 60 431 0
2023-02-03 157255 44 431 0
2023-02-02 157211 37 431 0
2023-02-01 157174 44 431 0
2023-01-31 157130 47 431 0
2023-01-30 157083 54 431 0
2023-01-27 157029 29 431 0
2023-01-26 157000 49 431 0
2023-01-25 156951 46 431 0
2023-01-24 156905 61 431 0
2023-01-23 156844 48 431 0
2023-01-20 156796 48 431 1
2023-01-19 156748 45 430 0
2023-01-18 156703 59 430 0
2023-01-17 156644 43 430 0
2023-01-16 156601 98 430 1
2023-01-13 156503 30 429 0
2023-01-12 156473 59 429 0
2023-01-11 156414 84 429 1
2023-01-10 156330 56 428 0
2023-01-09 156274 97 428 0
2023-01-06 156177 85 428 0
2023-01-05 156092 96 428 0
2023-01-04 155996 88 428 0
2023-01-03 155908 139 428 0
2023-01-02 155769 175 428 1
2022-12-30 155594 91 427 0
2022-12-29 155503 112 427 0
2022-12-28 155391 172 427 0
2022-12-27 155219 444 427 1
2022-12-23 154775 181 426 1
2022-12-22 154594 179 425 2
2022-12-21 154415 208 423 0
2022-12-20 154207 554 423 1
2022-12-19 153653 128 422 0
2022-12-18 153525 2 422 0
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2022-12-15 153388 182 422 0
2022-12-14 153206 270 422 0
2022-12-13 152936 147 422 0
2022-12-12 152789 298 422 1
2022-12-09 152491 134 421 0
2022-12-08 152357 157 421 0
2022-12-07 152200 175 421 0
2022-12-06 152025 179 421 0
2022-12-05 151846 239 421 2
2022-12-02 151607 123 419 0
2022-12-01 151484 151 419 0
2022-11-30 151333 183 419 0
2022-11-29 151150 194 419 1
2022-11-28 150956 214 418 0
2022-11-27 150742 1 418 0
2022-11-26 150741 2 418 0
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2022-11-09 149471 116 415 0
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2022-11-07 149210 159 415 0
2022-11-06 149051 2 415 0
2022-11-05 149049 1 415 0
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2022-11-02 148849 149 415 0
2022-11-01 148700 189 415 0
2022-10-31 148511 231 415 0
2022-10-29 148280 2 415 0
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2022-10-26 147939 222 415 0
2022-10-25 147717 220 415 0
2022-10-24 147497 333 415 0
2022-10-21 147164 184 415 0
2022-10-20 146980 219 415 0
2022-10-19 146761 325 415 1
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2022-10-17 146095 406 414 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: 0:03:04.048092