Germany: LK Paderborn (Nordrhein-Westfalen)¶

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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 14:01:03 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Paderborn", weeks=5);
2023-03-07T14:01:17.143634 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 7-day incidence rate (per 100K people) 86.3 LK Paderborn, 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Paderborn cases daily growth factor Germany-LK Paderborn cases daily growth factor (rolling mean) Germany-LK Paderborn 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 Paderborn deaths daily growth factor Germany-LK Paderborn deaths daily growth factor (rolling mean) Germany-LK Paderborn estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Paderborn doubling time cases (rolling mean) 0.0 61.9 123.8 185.6 daily change Germany-LK Paderborn new cases (rolling 7d mean) Germany-LK Paderborn new cases 0.000 0.309 0.619 0.928 daily change Germany-LK Paderborn new deaths (rolling 7d mean) Germany-LK Paderborn new deaths 0.000 0.212 0.423 0.635 0.847
In [4]:
overview(country="Germany", subregion="LK Paderborn");
2023-03-07T14:01:34.998800 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) 86.3 LK Paderborn, 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 0.0 0.5 1.0 1.5 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Paderborn cases daily growth factor Germany-LK Paderborn cases daily growth factor (rolling mean) Germany-LK Paderborn 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 Paderborn deaths daily growth factor Germany-LK Paderborn deaths daily growth factor (rolling mean) Germany-LK Paderborn 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 Paderborn doubling time cases (rolling mean) Germany-LK Paderborn doubling time deaths (rolling mean) 0 619 1238 daily change Germany-LK Paderborn new cases (rolling 7d mean) Germany-LK Paderborn new cases 0.000 1.547 3.094 4.641 daily change Germany-LK Paderborn new deaths (rolling 7d mean) Germany-LK Paderborn new deaths 0.0 50.0 100.1 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Paderborn", dates="2020-03-15:");
2023-03-07T14:04:32.116409 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 Paderborn LK Paderborn 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 Paderborn Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Paderborn")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Paderborn")
print(f'Population of country="Germany", subregion="LK Paderborn": {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 Paderborn": 309380 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 122573 37 258 0
2023-03-03 122536 4 258 0
2023-03-02 122532 52 258 0
2023-03-01 122480 77 258 0
2023-02-28 122403 97 258 0
2023-02-27 122306 133 258 0
2023-02-24 122173 67 258 0
2023-02-23 122106 114 258 1
2023-02-22 121992 82 257 0
2023-02-21 121910 208 257 1
2023-02-17 121702 52 256 0
2023-02-16 121650 72 256 1
2023-02-15 121578 69 255 0
2023-02-14 121509 98 255 0
2023-02-13 121411 68 255 0
2023-02-10 121343 26 255 0
2023-02-09 121317 43 255 1
2023-02-08 121274 57 254 0
2023-02-07 121217 64 254 0
2023-02-06 121153 50 254 0
2023-02-03 121103 26 254 0
2023-02-02 121077 32 254 0
2023-02-01 121045 38 254 0
2023-01-31 121007 47 254 0
2023-01-30 120960 42 254 0
2023-01-27 120918 22 254 0
2023-01-26 120896 52 254 0
2023-01-25 120844 32 254 0
2023-01-24 120812 49 254 0
2023-01-23 120763 42 254 0
2023-01-20 120721 21 254 0
2023-01-19 120700 24 254 0
2023-01-18 120676 30 254 0
2023-01-17 120646 43 254 0
2023-01-16 120603 33 254 1
2023-01-13 120570 21 253 0
2023-01-12 120549 72 253 0
2023-01-11 120477 24 253 0
2023-01-10 120453 54 253 0
2023-01-09 120399 93 253 0
2023-01-06 120306 71 253 1
2023-01-05 120235 93 252 1
2023-01-04 120142 80 251 0
2023-01-03 120062 98 251 0
2023-01-02 119964 113 251 0
2022-12-30 119851 56 251 0
2022-12-29 119795 126 251 2
2022-12-28 119669 135 249 0
2022-12-27 119534 216 249 0
2022-12-23 119318 102 249 0
2022-12-22 119216 240 249 0
2022-12-21 118976 161 249 2
2022-12-20 118815 145 247 1
2022-12-19 118670 139 246 0
2022-12-17 118531 64 246 0
2022-12-16 118467 121 246 0
2022-12-15 118346 93 246 0
2022-12-14 118253 128 246 1
2022-12-13 118125 171 245 0
2022-12-12 117954 149 245 0
2022-12-10 117805 50 245 0
2022-12-09 117755 50 245 1
2022-12-08 117705 178 244 1
2022-12-07 117527 130 243 0
2022-12-06 117397 144 243 1
2022-12-05 117253 115 242 0
2022-12-03 117138 59 242 0
2022-12-02 117079 148 242 0
2022-12-01 116931 69 242 0
2022-11-30 116862 184 242 0
2022-11-29 116678 131 242 0
2022-11-28 116547 97 242 1
2022-11-26 116450 123 241 0
2022-11-25 116327 87 241 0
2022-11-24 116240 105 241 0
2022-11-23 116135 207 241 0
2022-11-22 115928 149 241 0
2022-11-21 115779 187 241 2
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2022-11-15 115276 150 239 0
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2022-11-11 114934 170 239 1
2022-11-10 114764 107 238 0
2022-11-09 114657 254 238 0
2022-11-08 114403 184 238 1
2022-11-07 114219 171 237 0
2022-11-05 114048 139 237 0
2022-11-04 113909 242 237 1
2022-11-03 113667 284 236 0
2022-11-02 113383 331 236 0
2022-10-31 113052 356 236 0
2022-10-29 112696 128 236 0
2022-10-28 112568 195 236 0
2022-10-27 112373 400 236 0
2022-10-26 111973 305 236 1
2022-10-25 111668 319 235 0
2022-10-24 111349 269 235 1
2022-10-22 111080 197 234 0
2022-10-21 110883 327 234 0
2022-10-20 110556 206 234 0
2022-10-19 110350 459 234 2
2022-10-18 109891 358 232 0
2022-10-17 109533 336 232 0
2022-10-15 109197 71 232 0
2022-10-14 109126 362 232 0
2022-10-13 108764 526 232 0
2022-10-12 108238 170 232 0
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2022-10-10 107642 136 232 0
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2022-08-31 103071 234 232 1
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2022-08-29 102707 196 231 1
2022-08-27 102511 89 230 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: 0:03:54.155395