Germany: LK Südliche Weinstraße (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 15:11:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Südliche Weinstraße", weeks=5);
2023-03-07T15:11:22.388597 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) 58.4 LK Südliche Weinstraße, 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 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-LK Südliche Weinstraße cases daily growth factor Germany-LK Südliche Weinstraße cases daily growth factor (rolling mean) Germany-LK Südliche Weinstraße 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Südliche Weinstraße deaths daily growth factor Germany-LK Südliche Weinstraße deaths daily growth factor (rolling mean) Germany-LK Südliche Weinstraße 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 Südliche Weinstraße doubling time cases (rolling mean) 0.00 11.13 22.26 33.38 daily change Germany-LK Südliche Weinstraße new cases (rolling 7d mean) Germany-LK Südliche Weinstraße new cases 0.000 0.223 0.445 0.668 0.890 daily change Germany-LK Südliche Weinstraße new deaths (rolling 7d mean) Germany-LK Südliche Weinstraße new deaths 0.000 0.364 0.727
In [4]:
overview(country="Germany", subregion="LK Südliche Weinstraße");
2023-03-07T15:11:40.635096 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) 58.4 LK Südliche Weinstraße, 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 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Südliche Weinstraße cases daily growth factor Germany-LK Südliche Weinstraße cases daily growth factor (rolling mean) Germany-LK Südliche Weinstraße 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Südliche Weinstraße deaths daily growth factor Germany-LK Südliche Weinstraße deaths daily growth factor (rolling mean) Germany-LK Südliche Weinstraße 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-LK Südliche Weinstraße doubling time cases (rolling mean) Germany-LK Südliche Weinstraße doubling time deaths (rolling mean) 0.0 111.3 222.6 333.8 daily change Germany-LK Südliche Weinstraße new cases (rolling 7d mean) Germany-LK Südliche Weinstraße new cases 0.00 2.23 4.45 6.68 8.90 daily change Germany-LK Südliche Weinstraße new deaths (rolling 7d mean) Germany-LK Südliche Weinstraße new deaths 0.0 74.7 149.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Südliche Weinstraße", dates="2020-03-15:");
2023-03-07T15:14:16.101876 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 Südliche Weinstraße LK Südliche Weinstraße 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 Südliche Weinstraße Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Südliche Weinstraße")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Südliche Weinstraße")
print(f'Population of country="Germany", subregion="LK Südliche Weinstraße": {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 Südliche Weinstraße": 111279 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 46903 1 236 0
2023-03-05 46902 3 236 0
2023-03-04 46899 3 236 0
2023-03-03 46896 6 236 0
2023-03-02 46890 12 236 0
2023-03-01 46878 16 236 0
2023-02-28 46862 24 236 0
2023-02-27 46838 35 236 0
2023-02-26 46803 6 236 0
2023-02-25 46797 9 236 0
2023-02-24 46788 21 236 0
2023-02-23 46767 29 236 0
2023-02-22 46738 25 236 0
2023-02-21 46713 23 236 0
2023-02-20 46690 25 236 0
2023-02-19 46665 6 236 0
2023-02-18 46659 7 236 0
2023-02-17 46652 25 236 0
2023-02-16 46627 25 236 0
2023-02-15 46602 17 236 0
2023-02-14 46585 24 236 0
2023-02-13 46561 22 236 0
2023-02-12 46539 8 236 1
2023-02-11 46531 3 235 1
2023-02-10 46528 26 234 0
2023-02-09 46502 23 234 0
2023-02-08 46479 21 234 0
2023-02-07 46458 19 234 0
2023-02-06 46439 21 234 1
2023-02-05 46418 5 233 0
2023-02-04 46413 5 233 0
2023-02-03 46408 13 233 0
2023-02-02 46395 11 233 0
2023-02-01 46384 6 233 0
2023-01-31 46378 20 233 0
2023-01-30 46358 20 233 0
2023-01-29 46338 11 233 0
2023-01-28 46327 9 233 0
2023-01-27 46318 21 233 1
2023-01-26 46297 13 232 0
2023-01-25 46284 10 232 0
2023-01-24 46274 11 232 0
2023-01-23 46263 18 232 0
2023-01-22 46245 4 232 0
2023-01-21 46241 4 232 0
2023-01-20 46237 5 232 0
2023-01-19 46232 6 232 0
2023-01-18 46226 11 232 1
2023-01-17 46215 18 231 0
2023-01-16 46197 15 231 0
2023-01-15 46182 5 231 0
2023-01-14 46177 7 231 0
2023-01-13 46170 11 231 0
2023-01-12 46159 14 231 0
2023-01-11 46145 21 231 1
2023-01-10 46124 27 230 1
2023-01-09 46097 14 229 0
2023-01-08 46083 4 229 0
2023-01-07 46079 3 229 0
2023-01-06 46076 17 229 0
2023-01-05 46059 24 229 1
2023-01-04 46035 26 228 0
2023-01-03 46009 30 228 0
2023-01-02 45979 29 228 1
2022-12-31 45950 22 227 1
2022-12-30 45928 13 226 0
2022-12-29 45915 34 226 1
2022-12-28 45881 42 225 0
2022-12-27 45839 20 225 1
2022-12-26 45819 7 224 0
2022-12-25 45812 3 224 1
2022-12-24 45809 29 223 0
2022-12-23 45780 26 223 2
2022-12-22 45754 33 221 1
2022-12-21 45721 40 220 0
2022-12-20 45681 44 220 0
2022-12-19 45637 51 220 3
2022-12-18 45586 14 217 0
2022-12-17 45572 24 217 0
2022-12-16 45548 34 217 0
2022-12-15 45514 32 217 0
2022-12-14 45482 40 217 0
2022-12-13 45442 42 217 0
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2022-12-05 45254 31 217 0
2022-12-04 45223 7 217 0
2022-12-03 45216 15 217 0
2022-12-02 45201 13 217 0
2022-12-01 45188 25 217 0
2022-11-30 45163 23 217 0
2022-11-29 45140 17 217 0
2022-11-28 45123 29 217 0
2022-11-27 45094 11 217 1
2022-11-26 45083 12 216 0
2022-11-25 45071 22 216 0
2022-11-24 45049 37 216 0
2022-11-23 45012 28 216 0
2022-11-22 44984 63 216 0
2022-11-21 44921 26 216 0
2022-11-20 44895 15 216 0
2022-11-19 44880 16 216 0
2022-11-18 44864 42 216 0
2022-11-17 44822 51 216 0
2022-11-16 44771 43 216 0
2022-11-15 44728 34 216 0
2022-11-14 44694 34 216 0
2022-11-13 44660 29 216 0
2022-11-12 44631 16 216 0
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2022-11-08 44495 31 216 0
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2022-11-06 44429 13 216 1
2022-11-05 44416 19 215 0
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2022-11-01 44254 45 214 0
2022-10-31 44209 44 214 0
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2022-10-27 44001 30 214 0
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2022-10-25 43868 145 214 0
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2022-10-23 43656 41 214 1
2022-10-22 43615 56 213 0
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2022-10-19 43333 160 213 1
2022-10-18 43173 332 212 2
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2022-10-09 41345 50 202 0
2022-10-08 41295 47 202 0
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2022-10-06 41042 278 202 0
2022-10-05 40764 181 202 0
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2020-12-31 1542 21 50 1
2020-12-30 1521 40 49 3
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2020-12-28 1442 5 46 0
2020-12-27 1437 9 46 1
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2020-04-02 103 10 2 0
2020-04-01 93 12 2 0
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2020-03-20 31 4 0 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: 0:04:01.226498