Germany: LK Alzey-Worms (Rheinland-Pfalz)¶

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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 14:40:21 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Alzey-Worms", weeks=5);
2023-03-07T14:40:35.882729 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) 92.9 LK Alzey-Worms, 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 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 Alzey-Worms cases daily growth factor Germany-LK Alzey-Worms cases daily growth factor (rolling mean) Germany-LK Alzey-Worms 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 Alzey-Worms deaths daily growth factor Germany-LK Alzey-Worms deaths daily growth factor (rolling mean) Germany-LK Alzey-Worms 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 Alzey-Worms doubling time cases (rolling mean) 0.00 26.27 52.53 daily change Germany-LK Alzey-Worms new cases (rolling 7d mean) Germany-LK Alzey-Worms new cases 0.000 0.263 0.525 0.788 daily change Germany-LK Alzey-Worms new deaths (rolling 7d mean) Germany-LK Alzey-Worms new deaths 0.000 0.254 0.508 0.762
In [4]:
overview(country="Germany", subregion="LK Alzey-Worms");
2023-03-07T14:40:49.995722 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 7-day incidence rate (per 100K people) 92.9 LK Alzey-Worms, 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 0 200 400 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 Alzey-Worms cases daily growth factor Germany-LK Alzey-Worms cases daily growth factor (rolling mean) Germany-LK Alzey-Worms 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 Alzey-Worms deaths daily growth factor Germany-LK Alzey-Worms deaths daily growth factor (rolling mean) Germany-LK Alzey-Worms estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Alzey-Worms doubling time cases (rolling mean) Germany-LK Alzey-Worms doubling time deaths (rolling mean) 0.0 262.7 525.3 daily change Germany-LK Alzey-Worms new cases (rolling 7d mean) Germany-LK Alzey-Worms new cases 0.000 2.627 5.253 daily change Germany-LK Alzey-Worms new deaths (rolling 7d mean) Germany-LK Alzey-Worms new deaths 0.0 46.6 93.3 139.9 186.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Alzey-Worms", dates="2020-03-15:");
2023-03-07T14:42:57.281045 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 Alzey-Worms LK Alzey-Worms 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 Alzey-Worms Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Alzey-Worms")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Alzey-Worms")
print(f'Population of country="Germany", subregion="LK Alzey-Worms": {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 Alzey-Worms": 131330 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 57834 17 261 0
2023-03-03 57817 9 261 0
2023-03-02 57808 15 261 0
2023-03-01 57793 23 261 0
2023-02-28 57770 58 261 0
2023-02-27 57712 72 261 0
2023-02-24 57640 23 261 0
2023-02-23 57617 43 261 0
2023-02-22 57574 39 261 0
2023-02-21 57535 41 261 1
2023-02-20 57494 57 260 0
2023-02-17 57437 15 260 0
2023-02-16 57422 43 260 0
2023-02-15 57379 34 260 1
2023-02-14 57345 49 259 0
2023-02-13 57296 64 259 0
2023-02-10 57232 24 259 1
2023-02-09 57208 33 258 0
2023-02-08 57175 21 258 0
2023-02-07 57154 66 258 0
2023-02-06 57088 45 258 0
2023-02-03 57043 15 258 0
2023-02-02 57028 34 258 0
2023-02-01 56994 32 258 0
2023-01-31 56962 32 258 0
2023-01-30 56930 35 258 0
2023-01-27 56895 10 258 0
2023-01-26 56885 25 258 0
2023-01-25 56860 13 258 0
2023-01-24 56847 31 258 0
2023-01-23 56816 31 258 2
2023-01-20 56785 17 256 0
2023-01-19 56768 17 256 0
2023-01-18 56751 13 256 0
2023-01-17 56738 21 256 0
2023-01-16 56717 28 256 0
2023-01-13 56689 12 256 0
2023-01-12 56677 14 256 0
2023-01-11 56663 13 256 0
2023-01-10 56650 32 256 0
2023-01-09 56618 47 256 0
2023-01-06 56571 13 256 0
2023-01-05 56558 26 256 0
2023-01-04 56532 27 256 0
2023-01-03 56505 61 256 0
2023-01-02 56444 81 256 1
2022-12-30 56363 34 255 0
2022-12-29 56329 43 255 0
2022-12-28 56286 66 255 0
2022-12-27 56220 104 255 0
2022-12-23 56116 62 255 0
2022-12-22 56054 65 255 0
2022-12-21 55989 38 255 0
2022-12-20 55951 80 255 1
2022-12-19 55871 92 254 0
2022-12-16 55779 39 254 0
2022-12-15 55740 36 254 0
2022-12-14 55704 33 254 0
2022-12-13 55671 86 254 0
2022-12-12 55585 67 254 0
2022-12-09 55518 25 254 0
2022-12-08 55493 46 254 1
2022-12-07 55447 44 253 0
2022-12-06 55403 38 253 0
2022-12-05 55365 62 253 0
2022-12-02 55303 27 253 0
2022-12-01 55276 48 253 0
2022-11-30 55228 31 253 0
2022-11-29 55197 54 253 0
2022-11-28 55143 66 253 0
2022-11-25 55077 35 253 0
2022-11-24 55042 37 253 0
2022-11-23 55005 60 253 0
2022-11-22 54945 71 253 0
2022-11-21 54874 108 253 2
2022-11-18 54766 52 251 0
2022-11-17 54714 18 251 1
2022-11-16 54696 93 250 0
2022-11-15 54603 23 250 0
2022-11-14 54580 83 250 1
2022-11-11 54497 31 249 0
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2022-10-28 53693 44 249 1
2022-10-27 53649 133 248 0
2022-10-26 53516 149 248 0
2022-10-25 53367 126 248 1
2022-10-24 53241 219 247 2
2022-10-21 53022 136 245 0
2022-10-20 52886 534 245 0
2022-10-19 52352 619 245 0
2022-10-18 51733 275 245 1
2022-10-17 51458 438 244 0
2022-10-14 51020 217 244 1
2022-10-13 50803 348 243 1
2022-10-12 50455 51 242 0
2022-10-11 50404 168 242 0
2022-10-10 50236 289 242 2
2022-10-07 49947 129 240 0
2022-10-06 49818 207 240 2
2022-10-05 49611 219 238 2
2022-10-04 49392 94 236 1
2022-09-30 49298 161 235 0
2022-09-29 49137 198 235 0
2022-09-28 48939 287 235 0
2022-09-27 48652 341 235 0
2022-09-26 48311 40 235 0
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2022-09-22 48217 148 235 1
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2022-09-09 47000 84 227 0
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2022-08-31 46404 62 225 0
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2022-08-04 44866 157 221 2
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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: 0:02:55.281017