Germany: SK Berlin Friedrichshain-Kreuzberg (Berlin)¶

  • 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 11:58:37 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Berlin Friedrichshain-Kreuzberg", weeks=5);
2023-03-07T11:58:48.237743 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) 58.1 SK Berlin Friedrichshain-Kreuzberg, 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 0.2 0.4 0.6 daily change normalised per 100K 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 cases) Germany-SK Berlin Friedrichshain-Kreuzberg cases daily growth factor Germany-SK Berlin Friedrichshain-Kreuzberg cases daily growth factor (rolling mean) Germany-SK Berlin Friedrichshain-Kreuzberg 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 Berlin Friedrichshain-Kreuzberg deaths daily growth factor Germany-SK Berlin Friedrichshain-Kreuzberg deaths daily growth factor (rolling mean) Germany-SK Berlin Friedrichshain-Kreuzberg 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 Friedrichshain-Kreuzberg doubling time cases (rolling mean) 0.00 14.03 28.07 42.10 56.13 daily change Germany-SK Berlin Friedrichshain-Kreuzberg new cases (rolling 7d mean) Germany-SK Berlin Friedrichshain-Kreuzberg new cases 0.000 0.561 1.123 1.684 daily change Germany-SK Berlin Friedrichshain-Kreuzberg new deaths (rolling 7d mean) Germany-SK Berlin Friedrichshain-Kreuzberg new deaths 0.000 0.217 0.435 0.652 0.869
In [4]:
overview(country="Germany", subregion="SK Berlin Friedrichshain-Kreuzberg");
2023-03-07T11:59:08.621665 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) 58.1 SK Berlin Friedrichshain-Kreuzberg, 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 0.5 1.0 1.5 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Germany-SK Berlin Friedrichshain-Kreuzberg cases daily growth factor Germany-SK Berlin Friedrichshain-Kreuzberg cases daily growth factor (rolling mean) Germany-SK Berlin Friedrichshain-Kreuzberg 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 Berlin Friedrichshain-Kreuzberg deaths daily growth factor Germany-SK Berlin Friedrichshain-Kreuzberg deaths daily growth factor (rolling mean) Germany-SK Berlin Friedrichshain-Kreuzberg 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 Berlin Friedrichshain-Kreuzberg doubling time cases (rolling mean) Germany-SK Berlin Friedrichshain-Kreuzberg doubling time deaths (rolling mean) 0 561 1123 daily change Germany-SK Berlin Friedrichshain-Kreuzberg new cases (rolling 7d mean) Germany-SK Berlin Friedrichshain-Kreuzberg new cases 0.000 1.403 2.807 4.210 daily change Germany-SK Berlin Friedrichshain-Kreuzberg new deaths (rolling 7d mean) Germany-SK Berlin Friedrichshain-Kreuzberg new deaths 0.0 35.3 70.7 106.0 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Berlin Friedrichshain-Kreuzberg", dates="2020-03-15:");
2023-03-07T12:01:40.513941 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 Friedrichshain-Kreuzberg SK Berlin Friedrichshain-Kreuzberg 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 Friedrichshain-Kreuzberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Berlin Friedrichshain-Kreuzberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Berlin Friedrichshain-Kreuzberg")
print(f'Population of country="Germany", subregion="SK Berlin Friedrichshain-Kreuzberg": {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 Friedrichshain-Kreuzberg": 280671 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 122606 32 239 0
2023-03-02 122574 40 239 0
2023-03-01 122534 37 239 0
2023-02-28 122497 54 239 0
2023-02-27 122443 61 239 0
2023-02-24 122382 36 239 0
2023-02-23 122346 32 239 0
2023-02-22 122314 35 239 0
2023-02-21 122279 42 239 0
2023-02-20 122237 66 239 2
2023-02-17 122171 20 237 0
2023-02-16 122151 35 237 0
2023-02-15 122116 28 237 0
2023-02-14 122088 42 237 0
2023-02-13 122046 50 237 1
2023-02-10 121996 40 236 0
2023-02-09 121956 32 236 0
2023-02-08 121924 50 236 0
2023-02-07 121874 24 236 0
2023-02-06 121850 56 236 0
2023-02-03 121794 19 236 0
2023-02-02 121775 26 236 0
2023-02-01 121749 34 236 0
2023-01-31 121715 26 236 0
2023-01-30 121689 45 236 1
2023-01-27 121644 16 235 0
2023-01-26 121628 27 235 1
2023-01-25 121601 20 234 0
2023-01-24 121581 28 234 0
2023-01-23 121553 46 234 0
2023-01-20 121507 32 234 0
2023-01-19 121475 17 234 0
2023-01-18 121458 41 234 0
2023-01-17 121417 29 234 0
2023-01-16 121388 48 234 0
2023-01-13 121340 30 234 0
2023-01-12 121310 36 234 0
2023-01-11 121274 70 234 0
2023-01-10 121204 31 234 0
2023-01-09 121173 82 234 0
2023-01-06 121091 38 234 0
2023-01-05 121053 75 234 0
2023-01-04 120978 55 234 0
2023-01-03 120923 72 234 0
2023-01-02 120851 110 234 1
2022-12-30 120741 71 233 0
2022-12-29 120670 54 233 0
2022-12-28 120616 130 233 2
2022-12-27 120486 161 231 0
2022-12-23 120325 133 231 0
2022-12-22 120192 133 231 0
2022-12-21 120059 123 231 0
2022-12-20 119936 170 231 1
2022-12-19 119766 161 230 2
2022-12-17 119605 4 228 0
2022-12-16 119601 102 228 1
2022-12-15 119499 115 227 0
2022-12-14 119384 137 227 0
2022-12-13 119247 293 227 1
2022-12-12 118954 116 226 0
2022-12-09 118838 145 226 1
2022-12-08 118693 44 225 0
2022-12-07 118649 71 225 0
2022-12-06 118578 92 225 0
2022-12-05 118486 153 225 1
2022-12-04 118333 6 224 0
2022-12-03 118327 14 224 0
2022-12-02 118313 109 224 0
2022-12-01 118204 91 224 0
2022-11-30 118113 113 224 0
2022-11-29 118000 138 224 0
2022-11-28 117862 149 224 1
2022-11-27 117713 1 223 0
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2022-11-25 117695 93 223 1
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2022-11-11 116828 72 222 0
2022-11-10 116756 60 222 0
2022-11-09 116696 78 222 0
2022-11-08 116618 97 222 0
2022-11-07 116521 72 222 0
2022-11-06 116449 21 222 0
2022-11-05 116428 64 222 1
2022-11-04 116364 57 221 0
2022-11-03 116307 84 221 0
2022-11-02 116223 80 221 0
2022-11-01 116143 105 221 1
2022-10-31 116038 137 220 0
2022-10-29 115901 62 220 0
2022-10-28 115839 116 220 0
2022-10-27 115723 104 220 0
2022-10-26 115619 127 220 0
2022-10-25 115492 196 220 0
2022-10-24 115296 154 220 3
2022-10-23 115142 7 217 0
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2022-10-21 115029 150 217 0
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2022-09-11 109929 1 212 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: 0:03:22.720541