Germany: LK Würzburg (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 11:29:10 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Würzburg", weeks=5);
2023-03-07T11:29:24.566822 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) 103.3 LK Würzburg, 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 0.2 0.4 0.6 daily change normalised per 100K 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 cases) Germany-LK Würzburg cases daily growth factor Germany-LK Würzburg cases daily growth factor (rolling mean) Germany-LK Würzburg 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 Würzburg deaths daily growth factor Germany-LK Würzburg deaths daily growth factor (rolling mean) Germany-LK Würzburg 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 Würzburg doubling time cases (rolling mean) 0.0 32.7 65.4 98.1 daily change Germany-LK Würzburg new cases (rolling 7d mean) Germany-LK Würzburg new cases 0.000 0.327 0.654 0.981 daily change Germany-LK Würzburg new deaths (rolling 7d mean) Germany-LK Würzburg new deaths 0.000 0.378 0.756
In [4]:
overview(country="Germany", subregion="LK Würzburg");
2023-03-07T11:29:39.888815 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) 103.3 LK Würzburg, 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 6 daily change normalised per 100K 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 cases) Germany-LK Würzburg cases daily growth factor Germany-LK Würzburg cases daily growth factor (rolling mean) Germany-LK Würzburg 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 Würzburg deaths daily growth factor Germany-LK Würzburg deaths daily growth factor (rolling mean) Germany-LK Würzburg 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 Würzburg doubling time cases (rolling mean) Germany-LK Würzburg doubling time deaths (rolling mean) 0.0 327.1 654.1 daily change Germany-LK Würzburg new cases (rolling 7d mean) Germany-LK Würzburg new cases 0.00 3.27 6.54 9.81 daily change Germany-LK Würzburg new deaths (rolling 7d mean) Germany-LK Würzburg new deaths 0.0 55.4 110.8 166.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Würzburg", dates="2020-03-15:");
2023-03-07T11:32:09.818504 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 Würzburg LK Würzburg 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 Würzburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Würzburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Würzburg")
print(f'Population of country="Germany", subregion="LK Würzburg": {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 Würzburg": 163534 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 81898 29 207 0
2023-03-03 81869 11 207 0
2023-03-02 81858 21 207 0
2023-03-01 81837 48 207 0
2023-02-28 81789 60 207 0
2023-02-27 81729 129 207 0
2023-02-24 81600 32 207 0
2023-02-23 81568 52 207 0
2023-02-22 81516 29 207 0
2023-02-21 81487 16 207 0
2023-02-20 81471 18 207 0
2023-02-17 81453 13 207 0
2023-02-16 81440 42 207 0
2023-02-15 81398 54 207 0
2023-02-14 81344 61 207 0
2023-02-13 81283 74 207 1
2023-02-10 81209 28 206 0
2023-02-09 81181 32 206 0
2023-02-08 81149 41 206 0
2023-02-07 81108 69 206 0
2023-02-06 81039 38 206 0
2023-02-03 81001 28 206 0
2023-02-02 80973 32 206 0
2023-02-01 80941 29 206 0
2023-01-31 80912 24 206 0
2023-01-30 80888 25 206 0
2023-01-27 80863 18 206 0
2023-01-26 80845 20 206 0
2023-01-25 80825 11 206 0
2023-01-24 80814 29 206 0
2023-01-23 80785 23 206 0
2023-01-20 80762 10 206 1
2023-01-19 80752 15 205 0
2023-01-18 80737 17 205 0
2023-01-17 80720 13 205 0
2023-01-16 80707 40 205 0
2023-01-13 80667 16 205 0
2023-01-12 80651 18 205 0
2023-01-11 80633 26 205 0
2023-01-10 80607 43 205 1
2023-01-09 80564 44 204 0
2023-01-05 80520 17 204 0
2023-01-04 80503 21 204 0
2023-01-03 80482 33 204 0
2023-01-02 80449 59 204 0
2022-12-30 80390 47 204 0
2022-12-29 80343 26 204 0
2022-12-28 80317 42 204 0
2022-12-27 80275 109 204 1
2022-12-23 80166 31 203 0
2022-12-22 80135 50 203 0
2022-12-21 80085 44 203 1
2022-12-20 80041 57 202 0
2022-12-19 79984 75 202 1
2022-12-16 79909 28 201 0
2022-12-15 79881 33 201 0
2022-12-14 79848 37 201 0
2022-12-13 79811 56 201 0
2022-12-12 79755 71 201 0
2022-12-09 79684 36 201 0
2022-12-08 79648 33 201 0
2022-12-07 79615 26 201 0
2022-12-06 79589 66 201 0
2022-12-05 79523 69 201 2
2022-12-02 79454 26 199 0
2022-12-01 79428 38 199 0
2022-11-30 79390 36 199 0
2022-11-29 79354 70 199 0
2022-11-28 79284 63 199 0
2022-11-25 79221 31 199 1
2022-11-24 79190 46 198 0
2022-11-23 79144 44 198 0
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2022-11-18 78984 33 198 1
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2022-10-31 77963 145 196 1
2022-10-28 77818 86 195 0
2022-10-27 77732 115 195 1
2022-10-26 77617 153 194 0
2022-10-25 77464 216 194 0
2022-10-24 77248 320 194 0
2022-10-21 76928 113 194 0
2022-10-20 76815 209 194 0
2022-10-19 76606 155 194 1
2022-10-18 76451 292 193 2
2022-10-17 76159 411 191 0
2022-10-14 75748 159 191 0
2022-10-13 75589 247 191 2
2022-10-12 75342 259 189 0
2022-10-11 75083 317 189 0
2022-10-10 74766 447 189 2
2022-10-07 74319 244 187 0
2022-10-06 74075 420 187 0
2022-10-05 73655 234 187 0
2022-10-04 73421 396 187 1
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2022-09-27 72490 156 184 1
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2022-09-08 71074 51 180 0
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2022-09-06 70946 85 180 0
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2022-09-03 70732 1 180 0
2022-09-02 70731 42 180 1
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2022-08-31 70641 57 178 0
2022-08-30 70584 105 178 0
2022-08-29 70479 92 178 0
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2022-08-11 69435 104 172 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: 0:03:19.407374