Germany: SK Bremerhaven (Bremen)¶

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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 12:28:14 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Bremerhaven", weeks=5);
2023-03-07T12:28:28.270042 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 7-day incidence rate (per 100K people) 61.9 SK Bremerhaven, 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 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 0.8 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Bremerhaven cases daily growth factor Germany-SK Bremerhaven cases daily growth factor (rolling mean) Germany-SK Bremerhaven estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-SK Bremerhaven deaths daily growth factor Germany-SK Bremerhaven deaths daily growth factor (rolling mean) Germany-SK Bremerhaven 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 Bremerhaven doubling time cases (rolling mean) 0.00 11.32 22.63 daily change Germany-SK Bremerhaven new cases (rolling 7d mean) Germany-SK Bremerhaven new cases 0.000 0.226 0.453 0.679 0.905 daily change Germany-SK Bremerhaven new deaths (rolling 7d mean) Germany-SK Bremerhaven new deaths 0.000 0.274 0.549 0.823
In [4]:
overview(country="Germany", subregion="SK Bremerhaven");
2023-03-07T12:28:50.499858 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 61.9 SK Bremerhaven, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 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 1.4 1.4 R & growth factor (based on cases) Germany-SK Bremerhaven cases daily growth factor Germany-SK Bremerhaven cases daily growth factor (rolling mean) Germany-SK Bremerhaven 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 R & growth factor (based on deaths) Germany-SK Bremerhaven deaths daily growth factor Germany-SK Bremerhaven deaths daily growth factor (rolling mean) Germany-SK Bremerhaven 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 Bremerhaven doubling time cases (rolling mean) Germany-SK Bremerhaven doubling time deaths (rolling mean) 0.0 113.2 226.3 339.5 452.7 daily change Germany-SK Bremerhaven new cases (rolling 7d mean) Germany-SK Bremerhaven new cases 0.000 2.263 4.527 daily change Germany-SK Bremerhaven new deaths (rolling 7d mean) Germany-SK Bremerhaven new deaths 0.0 55.2 110.3 165.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Bremerhaven", dates="2020-03-15:");
2023-03-07T12:31:20.582355 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 Bremerhaven SK Bremerhaven 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 Bremerhaven Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Bremerhaven")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Bremerhaven")
print(f'Population of country="Germany", subregion="SK Bremerhaven": {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 Bremerhaven": 113173 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 53772 6 246 0
2023-03-03 53766 7 246 0
2023-03-02 53759 11 246 0
2023-03-01 53748 16 246 0
2023-02-28 53732 30 246 0
2023-02-27 53702 12 246 1
2023-02-24 53690 13 245 0
2023-02-23 53677 8 245 0
2023-02-22 53669 19 245 0
2023-02-21 53650 25 245 0
2023-02-20 53625 15 245 0
2023-02-17 53610 5 245 0
2023-02-16 53605 8 245 0
2023-02-15 53597 13 245 0
2023-02-14 53584 15 245 0
2023-02-13 53569 14 245 0
2023-02-10 53555 2 245 0
2023-02-09 53553 17 245 0
2023-02-08 53536 18 245 0
2023-02-07 53518 13 245 0
2023-02-06 53505 12 245 0
2023-02-03 53493 8 245 0
2023-02-02 53485 17 245 0
2023-02-01 53468 17 245 0
2023-01-31 53451 30 245 1
2023-01-30 53421 27 244 1
2023-01-27 53394 13 243 0
2023-01-26 53381 10 243 0
2023-01-25 53371 15 243 0
2023-01-24 53356 24 243 0
2023-01-23 53332 22 243 1
2023-01-20 53310 5 242 0
2023-01-19 53305 8 242 0
2023-01-18 53297 25 242 0
2023-01-17 53272 26 242 0
2023-01-16 53246 15 242 1
2023-01-13 53231 24 241 0
2023-01-12 53207 22 241 0
2023-01-11 53185 45 241 0
2023-01-10 53140 40 241 1
2023-01-09 53100 22 240 1
2023-01-07 53078 33 239 1
2023-01-06 53045 37 238 0
2023-01-05 53008 37 238 1
2023-01-04 52971 65 237 1
2023-01-03 52906 80 236 0
2023-01-02 52826 46 236 1
2022-12-30 52780 54 235 0
2022-12-29 52726 112 235 0
2022-12-28 52614 110 235 0
2022-12-27 52504 100 235 2
2022-12-26 52404 1 233 0
2022-12-23 52403 68 233 0
2022-12-22 52335 103 233 2
2022-12-21 52232 122 231 0
2022-12-20 52110 114 231 2
2022-12-19 51996 43 229 0
2022-12-17 51953 60 229 0
2022-12-16 51893 86 229 0
2022-12-15 51807 112 229 1
2022-12-14 51695 76 228 1
2022-12-13 51619 155 227 1
2022-12-12 51464 11 226 0
2022-12-10 51453 80 226 0
2022-12-09 51373 76 226 0
2022-12-08 51297 57 226 0
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2022-12-05 51017 27 226 0
2022-12-03 50990 35 226 0
2022-12-02 50955 52 226 0
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2022-11-03 49652 134 221 1
2022-11-02 49518 139 220 1
2022-11-01 49379 7 219 0
2022-10-31 49372 64 219 2
2022-10-29 49308 34 217 0
2022-10-28 49274 64 217 0
2022-10-27 49210 94 217 1
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2022-10-25 48998 151 215 0
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2022-10-22 48835 104 215 0
2022-10-21 48731 84 215 1
2022-10-20 48647 135 214 1
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2022-10-17 48183 54 211 2
2022-10-15 48129 103 209 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: 0:03:31.882254