Germany: LK Forchheim (Bayern)¶

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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 10:40:39 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Forchheim", weeks=5);
2023-03-07T10:40:49.334889 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) 83.1 LK Forchheim, 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 40 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-LK Forchheim cases daily growth factor Germany-LK Forchheim cases daily growth factor (rolling mean) Germany-LK Forchheim 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-LK Forchheim deaths daily growth factor Germany-LK Forchheim deaths daily growth factor (rolling mean) Germany-LK Forchheim 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-LK Forchheim doubling time cases (rolling mean) 0.00 11.68 23.35 35.03 46.70 daily change Germany-LK Forchheim new cases (rolling 7d mean) Germany-LK Forchheim new cases 0.000 0.584 1.168 1.751 daily change Germany-LK Forchheim new deaths (rolling 7d mean) Germany-LK Forchheim new deaths 0.000 0.203 0.406 0.609 0.812
In [4]:
overview(country="Germany", subregion="LK Forchheim");
2023-03-07T10:41:07.092587 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 4000 4000 7-day incidence rate (per 100K people) 83.1 LK Forchheim, 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 0 250 500 750 1000 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5 10 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-LK Forchheim cases daily growth factor Germany-LK Forchheim cases daily growth factor (rolling mean) Germany-LK Forchheim 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-LK Forchheim deaths daily growth factor Germany-LK Forchheim deaths daily growth factor (rolling mean) Germany-LK Forchheim 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-LK Forchheim doubling time cases (rolling mean) Germany-LK Forchheim doubling time deaths (rolling mean) 0 292 584 876 1168 daily change Germany-LK Forchheim new cases (rolling 7d mean) Germany-LK Forchheim new cases 0.00 5.84 11.68 daily change Germany-LK Forchheim new deaths (rolling 7d mean) Germany-LK Forchheim new deaths 0.0 59.4 118.8 178.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Forchheim", dates="2020-03-15:");
2023-03-07T10:43:45.238743 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: LK Forchheim LK Forchheim 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 LK Forchheim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Forchheim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Forchheim")
print(f'Population of country="Germany", subregion="LK Forchheim": {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="LK Forchheim": 116753 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 62555 6 249 0
2023-03-05 62549 2 249 0
2023-03-04 62547 2 249 0
2023-03-03 62545 19 249 0
2023-03-02 62526 13 249 0
2023-03-01 62513 18 249 0
2023-02-28 62495 37 249 0
2023-02-27 62458 34 249 0
2023-02-26 62424 12 249 0
2023-02-25 62412 14 249 0
2023-02-24 62398 27 249 0
2023-02-23 62371 34 249 0
2023-02-22 62337 24 249 0
2023-02-21 62313 41 249 0
2023-02-20 62272 29 249 0
2023-02-19 62243 11 249 0
2023-02-18 62232 18 249 0
2023-02-17 62214 40 249 0
2023-02-16 62174 38 249 1
2023-02-15 62136 38 248 0
2023-02-14 62098 56 248 0
2023-02-13 62042 31 248 2
2023-02-12 62011 5 246 0
2023-02-11 62006 7 246 0
2023-02-10 61999 37 246 0
2023-02-09 61962 45 246 1
2023-02-08 61917 28 245 0
2023-02-07 61889 37 245 0
2023-02-06 61852 39 245 0
2023-02-05 61813 8 245 0
2023-02-04 61805 9 245 0
2023-02-03 61796 29 245 0
2023-02-02 61767 27 245 0
2023-02-01 61740 27 245 0
2023-01-31 61713 36 245 0
2023-01-30 61677 38 245 0
2023-01-29 61639 9 245 0
2023-01-28 61630 14 245 0
2023-01-27 61616 9 245 0
2023-01-26 61607 11 245 0
2023-01-25 61596 11 245 0
2023-01-24 61585 24 245 0
2023-01-23 61561 24 245 0
2023-01-22 61537 4 245 0
2023-01-21 61533 9 245 0
2023-01-20 61524 21 245 1
2023-01-19 61503 15 244 0
2023-01-18 61488 10 244 0
2023-01-17 61478 27 244 0
2023-01-16 61451 23 244 0
2023-01-15 61428 1 244 0
2023-01-14 61427 10 244 0
2023-01-13 61417 15 244 0
2023-01-12 61402 16 244 0
2023-01-11 61386 22 244 0
2023-01-10 61364 36 244 0
2023-01-09 61328 18 244 0
2023-01-08 61310 7 244 0
2023-01-07 61303 9 244 0
2023-01-06 61294 12 244 0
2023-01-05 61282 34 244 1
2023-01-04 61248 25 243 1
2023-01-03 61223 35 242 0
2023-01-02 61188 24 242 0
2023-01-01 61164 5 242 0
2022-12-31 61159 18 242 0
2022-12-30 61141 23 242 0
2022-12-29 61118 28 242 0
2022-12-28 61090 39 242 0
2022-12-27 61051 39 242 0
2022-12-26 61012 7 242 0
2022-12-25 61005 10 242 0
2022-12-24 60995 20 242 1
2022-12-23 60975 31 241 0
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2022-11-30 60326 19 239 0
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2022-11-26 60240 10 239 0
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2022-11-24 60213 21 239 1
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2022-11-20 60146 7 238 0
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2022-11-18 60129 15 238 0
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2022-10-26 59344 92 238 1
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2022-10-21 58926 117 234 2
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2020-12-10 1553 85 46 17
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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: 0:03:15.954232