Germany: SK Erlangen (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 11:36:47 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Erlangen", weeks=5);
2023-03-07T11:36:59.042883 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) 67.1 SK Erlangen, 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 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Erlangen cases daily growth factor Germany-SK Erlangen cases daily growth factor (rolling mean) Germany-SK Erlangen 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 Erlangen deaths daily growth factor Germany-SK Erlangen deaths daily growth factor (rolling mean) Germany-SK Erlangen 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 Erlangen doubling time cases (rolling mean) 0.00 22.66 45.32 daily change Germany-SK Erlangen new cases (rolling 7d mean) Germany-SK Erlangen new cases 0.000 0.227 0.453 0.680 0.906 daily change Germany-SK Erlangen new deaths (rolling 7d mean) Germany-SK Erlangen new deaths 0.000 0.389 0.778
In [4]:
overview(country="Germany", subregion="SK Erlangen");
2023-03-07T11:37:14.840978 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) 67.1 SK Erlangen, 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Erlangen cases daily growth factor Germany-SK Erlangen cases daily growth factor (rolling mean) Germany-SK Erlangen 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 Erlangen deaths daily growth factor Germany-SK Erlangen deaths daily growth factor (rolling mean) Germany-SK Erlangen 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 Erlangen doubling time cases (rolling mean) Germany-SK Erlangen doubling time deaths (rolling mean) 0.0 113.3 226.6 339.9 453.2 daily change Germany-SK Erlangen new cases (rolling 7d mean) Germany-SK Erlangen new cases 0.000 2.266 4.532 daily change Germany-SK Erlangen new deaths (rolling 7d mean) Germany-SK Erlangen new deaths 0.0 34.6 69.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Erlangen", dates="2020-03-15:");
2023-03-07T11:39:23.267759 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 Erlangen SK Erlangen 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 Erlangen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Erlangen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Erlangen")
print(f'Population of country="Germany", subregion="SK Erlangen": {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 Erlangen": 113292 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 47352 10 157 0
2023-03-03 47342 3 157 0
2023-03-02 47339 8 157 0
2023-03-01 47331 24 157 0
2023-02-28 47307 31 157 0
2023-02-27 47276 62 157 0
2023-02-24 47214 20 157 0
2023-02-23 47194 31 157 0
2023-02-22 47163 21 157 0
2023-02-21 47142 53 157 0
2023-02-20 47089 65 157 0
2023-02-18 47024 1 157 0
2023-02-17 47023 18 157 0
2023-02-16 47005 44 157 0
2023-02-15 46961 39 157 0
2023-02-14 46922 34 157 0
2023-02-13 46888 62 157 0
2023-02-10 46826 26 157 0
2023-02-09 46800 27 157 0
2023-02-08 46773 30 157 0
2023-02-07 46743 27 157 0
2023-02-06 46716 39 157 1
2023-02-03 46677 16 156 0
2023-02-02 46661 17 156 0
2023-02-01 46644 23 156 0
2023-01-31 46621 26 156 0
2023-01-30 46595 28 156 0
2023-01-27 46567 13 156 0
2023-01-26 46554 19 156 0
2023-01-25 46535 25 156 0
2023-01-24 46510 20 156 0
2023-01-23 46490 25 156 0
2023-01-20 46465 18 156 0
2023-01-19 46447 11 156 0
2023-01-18 46436 11 156 0
2023-01-17 46425 17 156 0
2023-01-16 46408 21 156 0
2023-01-15 46387 1 156 0
2023-01-13 46386 10 156 0
2023-01-12 46376 9 156 0
2023-01-11 46367 16 156 0
2023-01-10 46351 23 156 0
2023-01-09 46328 44 156 0
2023-01-05 46284 25 156 1
2023-01-04 46259 27 155 1
2023-01-03 46232 35 154 0
2023-01-02 46197 31 154 0
2022-12-30 46166 19 154 0
2022-12-29 46147 25 154 0
2022-12-28 46122 37 154 0
2022-12-27 46085 77 154 0
2022-12-24 46008 1 154 0
2022-12-23 46007 33 154 0
2022-12-22 45974 41 154 0
2022-12-21 45933 37 154 0
2022-12-20 45896 59 154 0
2022-12-19 45837 54 154 0
2022-12-18 45783 1 154 0
2022-12-17 45782 8 154 0
2022-12-16 45774 25 154 0
2022-12-15 45749 36 154 0
2022-12-14 45713 24 154 0
2022-12-13 45689 36 154 1
2022-12-12 45653 55 153 0
2022-12-10 45598 1 153 0
2022-12-09 45597 23 153 1
2022-12-08 45574 27 152 0
2022-12-07 45547 30 152 1
2022-12-06 45517 40 151 0
2022-12-05 45477 35 151 0
2022-12-03 45442 2 151 0
2022-12-02 45440 22 151 0
2022-12-01 45418 29 151 0
2022-11-30 45389 11 151 0
2022-11-29 45378 35 151 0
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2022-11-07 44909 39 151 0
2022-11-05 44870 2 151 0
2022-11-04 44868 23 151 0
2022-11-03 44845 43 151 0
2022-11-02 44802 66 151 0
2022-11-01 44736 3 151 0
2022-10-31 44733 68 151 0
2022-10-29 44665 1 151 0
2022-10-28 44664 48 151 0
2022-10-27 44616 53 151 0
2022-10-26 44563 54 151 0
2022-10-25 44509 71 151 0
2022-10-24 44438 91 151 0
2022-10-21 44347 64 151 0
2022-10-20 44283 57 151 0
2022-10-19 44226 108 151 0
2022-10-18 44118 225 151 1
2022-10-17 43893 175 150 0
2022-10-16 43718 1 150 0
2022-10-15 43717 12 150 0
2022-10-14 43705 73 150 1
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2022-10-12 43479 112 149 0
2022-10-11 43367 224 149 0
2022-10-10 43143 217 149 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: 0:02:47.705905