Germany: LK Hersfeld-Rotenburg (Hessen)¶

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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 12:28:12 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Hersfeld-Rotenburg", weeks=5);
2023-03-07T12:28:26.365485 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) 59.1 LK Hersfeld-Rotenburg, 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 0.2 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-LK Hersfeld-Rotenburg cases daily growth factor Germany-LK Hersfeld-Rotenburg cases daily growth factor (rolling mean) Germany-LK Hersfeld-Rotenburg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Hersfeld-Rotenburg deaths daily growth factor Germany-LK Hersfeld-Rotenburg deaths daily growth factor (rolling mean) Germany-LK Hersfeld-Rotenburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Hersfeld-Rotenburg doubling time cases (rolling mean) 0.00 12.02 24.03 36.05 48.07 daily change Germany-LK Hersfeld-Rotenburg new cases (rolling 7d mean) Germany-LK Hersfeld-Rotenburg new cases 0.000 0.240 0.481 0.721 0.961 daily change Germany-LK Hersfeld-Rotenburg new deaths (rolling 7d mean) Germany-LK Hersfeld-Rotenburg new deaths 0.000 0.344 0.688
In [4]:
overview(country="Germany", subregion="LK Hersfeld-Rotenburg");
2023-03-07T12:28:45.717216 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 7-day incidence rate (per 100K people) 59.1 LK Hersfeld-Rotenburg, 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 6 8 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-LK Hersfeld-Rotenburg cases daily growth factor Germany-LK Hersfeld-Rotenburg cases daily growth factor (rolling mean) Germany-LK Hersfeld-Rotenburg estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 8 8 R & growth factor (based on deaths) Germany-LK Hersfeld-Rotenburg deaths daily growth factor Germany-LK Hersfeld-Rotenburg deaths daily growth factor (rolling mean) Germany-LK Hersfeld-Rotenburg 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 8000 cases doubling time [days] Germany-LK Hersfeld-Rotenburg doubling time cases (rolling mean) Germany-LK Hersfeld-Rotenburg doubling time deaths (rolling mean) 0.0 120.2 240.3 360.5 480.7 daily change Germany-LK Hersfeld-Rotenburg new cases (rolling 7d mean) Germany-LK Hersfeld-Rotenburg new cases 0.00 2.40 4.81 7.21 9.61 daily change Germany-LK Hersfeld-Rotenburg new deaths (rolling 7d mean) Germany-LK Hersfeld-Rotenburg new deaths 0 37 74 111 148 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Hersfeld-Rotenburg", dates="2020-03-15:");
2023-03-07T12:31:07.941812 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 Hersfeld-Rotenburg LK Hersfeld-Rotenburg 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 Hersfeld-Rotenburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Hersfeld-Rotenburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Hersfeld-Rotenburg")
print(f'Population of country="Germany", subregion="LK Hersfeld-Rotenburg": {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 Hersfeld-Rotenburg": 120163 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 53133 9 233 0
2023-03-03 53124 1 233 0
2023-03-02 53123 23 233 0
2023-03-01 53100 15 233 0
2023-02-28 53085 23 233 0
2023-02-27 53062 56 233 0
2023-02-24 53006 10 233 0
2023-02-23 52996 29 233 0
2023-02-22 52967 34 233 1
2023-02-21 52933 19 232 1
2023-02-20 52914 26 231 0
2023-02-17 52888 25 231 0
2023-02-16 52863 17 231 0
2023-02-15 52846 33 231 0
2023-02-14 52813 33 231 0
2023-02-13 52780 30 231 0
2023-02-10 52750 14 231 0
2023-02-09 52736 14 231 0
2023-02-08 52722 16 231 0
2023-02-07 52706 18 231 0
2023-02-06 52688 33 231 0
2023-02-03 52655 21 231 0
2023-02-02 52634 11 231 0
2023-02-01 52623 18 231 0
2023-01-31 52605 21 231 0
2023-01-30 52584 34 231 0
2023-01-27 52550 12 231 0
2023-01-26 52538 13 231 0
2023-01-25 52525 21 231 0
2023-01-24 52504 31 231 0
2023-01-23 52473 10 231 0
2023-01-20 52463 12 231 0
2023-01-19 52451 7 231 0
2023-01-18 52444 9 231 0
2023-01-17 52435 14 231 0
2023-01-16 52421 22 231 0
2023-01-13 52399 4 231 0
2023-01-12 52395 12 231 0
2023-01-11 52383 13 231 0
2023-01-10 52370 20 231 0
2023-01-09 52350 25 231 0
2023-01-06 52325 14 231 0
2023-01-05 52311 27 231 0
2023-01-04 52284 31 231 0
2023-01-03 52253 35 231 0
2023-01-02 52218 45 231 0
2022-12-30 52173 39 231 0
2022-12-29 52134 44 231 0
2022-12-28 52090 5 231 0
2022-12-27 52085 14 231 0
2022-12-26 52071 56 231 0
2022-12-23 52015 34 231 0
2022-12-22 51981 32 231 0
2022-12-21 51949 52 231 0
2022-12-20 51897 46 231 0
2022-12-19 51851 44 231 0
2022-12-16 51807 13 231 0
2022-12-15 51794 26 231 0
2022-12-14 51768 53 231 0
2022-12-13 51715 35 231 0
2022-12-12 51680 57 231 0
2022-12-09 51623 29 231 0
2022-12-08 51594 23 231 0
2022-12-07 51571 39 231 0
2022-12-06 51532 35 231 0
2022-12-05 51497 49 231 0
2022-12-02 51448 19 231 0
2022-12-01 51429 12 231 0
2022-11-30 51417 21 231 0
2022-11-29 51396 27 231 0
2022-11-28 51369 53 231 0
2022-11-25 51316 11 231 0
2022-11-24 51305 48 231 0
2022-11-23 51257 28 231 1
2022-11-22 51229 58 230 0
2022-11-21 51171 52 230 1
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2022-11-03 50658 49 229 0
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2022-10-31 50439 137 229 0
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2022-10-24 49791 262 229 0
2022-10-23 49529 4 229 0
2022-10-22 49525 1 229 0
2022-10-21 49524 155 229 0
2022-10-20 49369 214 229 0
2022-10-19 49155 363 229 0
2022-10-18 48792 393 229 1
2022-10-17 48399 255 228 0
2022-10-16 48144 1 228 0
2022-10-14 48143 314 228 0
2022-10-13 47829 274 228 0
2022-10-12 47555 237 228 0
2022-10-11 47318 213 228 0
2022-10-10 47105 202 228 0
2022-10-08 46903 3 228 0
2022-10-07 46900 160 228 0
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2022-10-04 46183 235 228 1
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2020-12-29 2052 48 89 2
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2020-12-11 1367 63 59 10
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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: 0:03:07.848632