Germany: LK Dachau (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 10:40:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Dachau", weeks=5);
2023-03-07T10:41:00.980351 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 200 200 7-day incidence rate (per 100K people) 45.7 LK Dachau, 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 0.5 1.0 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 Dachau cases daily growth factor Germany-LK Dachau cases daily growth factor (rolling mean) Germany-LK Dachau 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 Dachau deaths daily growth factor Germany-LK Dachau deaths daily growth factor (rolling mean) Germany-LK Dachau 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-LK Dachau doubling time cases (rolling mean) Germany-LK Dachau doubling time deaths (rolling mean) 0.00 31.09 62.18 daily change Germany-LK Dachau new cases (rolling 7d mean) Germany-LK Dachau new cases 0.000 0.777 1.554 daily change Germany-LK Dachau new deaths (rolling 7d mean) Germany-LK Dachau new deaths 0.0 113.7 227.4 341.0 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Dachau");
2023-03-07T10:41:18.292571 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 1000 1000 2000 2000 7-day incidence rate (per 100K people) 45.7 LK Dachau, Germany, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 200 400 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 1 2 3 4 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Dachau cases daily growth factor Germany-LK Dachau cases daily growth factor (rolling mean) Germany-LK Dachau estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Dachau deaths daily growth factor Germany-LK Dachau deaths daily growth factor (rolling mean) Germany-LK Dachau estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Dachau doubling time cases (rolling mean) Germany-LK Dachau doubling time deaths (rolling mean) 0.0 310.9 621.8 daily change Germany-LK Dachau new cases (rolling 7d mean) Germany-LK Dachau new cases 0.000 1.554 3.109 4.663 6.218 daily change Germany-LK Dachau new deaths (rolling 7d mean) Germany-LK Dachau new deaths 0.0 62.0 124.0 185.9 247.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Dachau", dates="2020-03-15:");
2023-03-07T10:43:50.121842 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 Dachau LK Dachau 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 Dachau Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Dachau")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Dachau")
print(f'Population of country="Germany", subregion="LK Dachau": {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 Dachau": 155449 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 85375 13 283 0
2023-03-02 85362 11 283 0
2023-03-01 85351 19 283 0
2023-02-28 85332 28 283 0
2023-02-27 85304 44 283 1
2023-02-24 85260 17 282 0
2023-02-23 85243 32 282 0
2023-02-22 85211 26 282 0
2023-02-21 85185 44 282 0
2023-02-20 85141 48 282 0
2023-02-17 85093 16 282 0
2023-02-16 85077 22 282 0
2023-02-15 85055 55 282 0
2023-02-14 85000 68 282 1
2023-02-13 84932 48 281 0
2023-02-10 84884 37 281 0
2023-02-09 84847 36 281 0
2023-02-08 84811 54 281 1
2023-02-07 84757 71 280 1
2023-02-06 84686 64 279 2
2023-02-03 84622 49 277 1
2023-02-02 84573 52 276 0
2023-02-01 84521 50 276 0
2023-01-31 84471 84 276 1
2023-01-30 84387 67 275 0
2023-01-27 84320 24 275 0
2023-01-26 84296 24 275 2
2023-01-25 84272 18 273 0
2023-01-24 84254 44 273 0
2023-01-23 84210 29 273 1
2023-01-20 84181 9 272 0
2023-01-19 84172 21 272 0
2023-01-18 84151 19 272 0
2023-01-17 84132 29 272 0
2023-01-16 84103 28 272 0
2023-01-13 84075 14 272 0
2023-01-12 84061 15 272 0
2023-01-11 84046 26 272 1
2023-01-10 84020 46 271 0
2023-01-09 83974 53 271 1
2023-01-05 83921 24 270 0
2023-01-04 83897 30 270 0
2023-01-03 83867 37 270 0
2023-01-02 83830 54 270 2
2022-12-30 83776 26 268 0
2022-12-29 83750 34 268 0
2022-12-28 83716 48 268 1
2022-12-27 83668 64 267 1
2022-12-23 83604 45 266 0
2022-12-22 83559 91 266 0
2022-12-21 83468 44 266 0
2022-12-20 83424 50 266 0
2022-12-19 83374 53 266 1
2022-12-16 83321 27 265 0
2022-12-15 83294 27 265 1
2022-12-14 83267 72 264 2
2022-12-13 83195 44 262 0
2022-12-12 83151 47 262 1
2022-12-09 83104 27 261 1
2022-12-08 83077 44 260 1
2022-12-07 83033 30 259 0
2022-12-06 83003 46 259 0
2022-12-05 82957 36 259 0
2022-12-02 82921 36 259 0
2022-12-01 82885 20 259 0
2022-11-30 82865 37 259 0
2022-11-29 82828 63 259 0
2022-11-28 82765 44 259 0
2022-11-25 82721 35 259 0
2022-11-24 82686 28 259 0
2022-11-23 82658 32 259 0
2022-11-22 82626 56 259 1
2022-11-21 82570 46 258 0
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2022-10-31 81895 62 258 0
2022-10-28 81833 28 258 0
2022-10-27 81805 63 258 0
2022-10-26 81742 75 258 0
2022-10-25 81667 121 258 1
2022-10-24 81546 157 257 2
2022-10-21 81389 94 255 0
2022-10-20 81295 113 255 0
2022-10-19 81182 129 255 0
2022-10-18 81053 219 255 0
2022-10-17 80834 298 255 1
2022-10-14 80536 187 254 0
2022-10-13 80349 229 254 0
2022-10-12 80120 291 254 0
2022-10-11 79829 371 254 0
2022-10-10 79458 530 254 0
2022-10-07 78928 266 254 0
2022-10-06 78662 443 254 1
2022-10-05 78219 603 253 1
2022-10-04 77616 596 252 1
2022-10-03 77020 1 251 0
2022-09-30 77019 263 251 1
2022-09-29 76756 292 250 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: 0:03:13.810370