Germany: LK Darmstadt-Dieburg (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 12:28:43 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Darmstadt-Dieburg", weeks=5);
2023-03-07T12:28:55.903114 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 91.3 LK Darmstadt-Dieburg, 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 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 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 Darmstadt-Dieburg cases daily growth factor Germany-LK Darmstadt-Dieburg cases daily growth factor (rolling mean) Germany-LK Darmstadt-Dieburg estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Darmstadt-Dieburg deaths daily growth factor Germany-LK Darmstadt-Dieburg deaths daily growth factor (rolling mean) Germany-LK Darmstadt-Dieburg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Darmstadt-Dieburg doubling time cases (rolling mean) 0.0 59.4 118.8 178.1 daily change Germany-LK Darmstadt-Dieburg new cases (rolling 7d mean) Germany-LK Darmstadt-Dieburg new cases 0.000 0.297 0.594 0.891 daily change Germany-LK Darmstadt-Dieburg new deaths (rolling 7d mean) Germany-LK Darmstadt-Dieburg new deaths 0.000 0.284 0.569 0.853
In [4]:
overview(country="Germany", subregion="LK Darmstadt-Dieburg");
2023-03-07T12:29:14.339606 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) 91.3 LK Darmstadt-Dieburg, 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 R & growth factor (based on cases) Germany-LK Darmstadt-Dieburg cases daily growth factor Germany-LK Darmstadt-Dieburg cases daily growth factor (rolling mean) Germany-LK Darmstadt-Dieburg estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Darmstadt-Dieburg deaths daily growth factor Germany-LK Darmstadt-Dieburg deaths daily growth factor (rolling mean) Germany-LK Darmstadt-Dieburg 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 Darmstadt-Dieburg doubling time cases (rolling mean) Germany-LK Darmstadt-Dieburg doubling time deaths (rolling mean) 0 297 594 891 1188 daily change Germany-LK Darmstadt-Dieburg new cases (rolling 7d mean) Germany-LK Darmstadt-Dieburg new cases 0.00 5.94 11.88 daily change Germany-LK Darmstadt-Dieburg new deaths (rolling 7d mean) Germany-LK Darmstadt-Dieburg new deaths 0.0 184.4 368.9 553.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Darmstadt-Dieburg", dates="2020-03-15:");
2023-03-07T12:31:12.452078 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 Darmstadt-Dieburg LK Darmstadt-Dieburg 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 Darmstadt-Dieburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Darmstadt-Dieburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Darmstadt-Dieburg")
print(f'Population of country="Germany", subregion="LK Darmstadt-Dieburg": {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 Darmstadt-Dieburg": 296900 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 131429 52 569 1
2023-03-03 131377 27 568 0
2023-03-02 131350 31 568 0
2023-03-01 131319 60 568 0
2023-02-28 131259 101 568 0
2023-02-27 131158 193 568 1
2023-02-24 130965 55 567 0
2023-02-23 130910 107 567 0
2023-02-22 130803 113 567 1
2023-02-21 130690 53 566 0
2023-02-20 130637 150 566 1
2023-02-17 130487 78 565 0
2023-02-16 130409 64 565 0
2023-02-15 130345 88 565 0
2023-02-14 130257 81 565 0
2023-02-13 130176 157 565 1
2023-02-10 130019 53 564 0
2023-02-09 129966 36 564 0
2023-02-08 129930 83 564 0
2023-02-07 129847 79 564 1
2023-02-06 129768 106 563 0
2023-02-03 129662 55 563 1
2023-02-02 129607 53 562 0
2023-02-01 129554 69 562 0
2023-01-31 129485 56 562 0
2023-01-30 129429 82 562 0
2023-01-27 129347 22 562 0
2023-01-26 129325 58 562 0
2023-01-25 129267 37 562 0
2023-01-24 129230 85 562 2
2023-01-23 129145 74 560 0
2023-01-20 129071 46 560 1
2023-01-19 129025 29 559 0
2023-01-18 128996 51 559 1
2023-01-17 128945 66 558 1
2023-01-16 128879 67 557 0
2023-01-13 128812 44 557 0
2023-01-12 128768 55 557 0
2023-01-11 128713 58 557 0
2023-01-10 128655 108 557 0
2023-01-09 128547 100 557 1
2023-01-06 128447 61 556 0
2023-01-05 128386 74 556 1
2023-01-04 128312 99 555 2
2023-01-03 128213 134 553 0
2023-01-02 128079 169 553 1
2022-12-30 127910 66 552 0
2022-12-29 127844 112 552 1
2022-12-28 127732 164 551 1
2022-12-27 127568 281 550 0
2022-12-23 127287 163 550 0
2022-12-22 127124 127 550 1
2022-12-21 126997 96 549 0
2022-12-20 126901 215 549 0
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2022-12-16 126567 102 549 0
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2022-12-14 126365 164 549 1
2022-12-13 126201 148 548 2
2022-12-12 126053 217 546 0
2022-12-09 125836 63 546 2
2022-12-08 125773 99 544 1
2022-12-07 125674 82 543 1
2022-12-06 125592 146 542 0
2022-12-05 125446 148 542 2
2022-12-02 125298 51 540 0
2022-12-01 125247 102 540 0
2022-11-30 125145 82 540 0
2022-11-29 125063 79 540 0
2022-11-28 124984 126 540 1
2022-11-25 124858 117 539 0
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2022-11-21 124396 223 539 1
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2022-11-01 122356 273 534 1
2022-10-31 122083 396 533 2
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2022-10-20 119826 339 527 3
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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: 0:02:37.960618