Germany: LK Fürstenfeldbruck (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 10:47:51 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Fürstenfeldbruck", weeks=5);
2023-03-07T10:48:03.408287 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) 61.8 LK Fürstenfeldbruck, 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 40 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 0.4 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 Fürstenfeldbruck cases daily growth factor Germany-LK Fürstenfeldbruck cases daily growth factor (rolling mean) Germany-LK Fürstenfeldbruck estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Fürstenfeldbruck deaths daily growth factor Germany-LK Fürstenfeldbruck deaths daily growth factor (rolling mean) Germany-LK Fürstenfeldbruck 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 Fürstenfeldbruck doubling time cases (rolling mean) 0.0 21.9 43.7 65.6 87.4 daily change Germany-LK Fürstenfeldbruck new cases (rolling 7d mean) Germany-LK Fürstenfeldbruck new cases 0.000 0.219 0.437 0.656 0.874 daily change Germany-LK Fürstenfeldbruck new deaths (rolling 7d mean) Germany-LK Fürstenfeldbruck new deaths 0.000 0.314 0.628 0.942
In [4]:
overview(country="Germany", subregion="LK Fürstenfeldbruck");
2023-03-07T10:48:21.314151 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 3000 3000 4000 4000 7-day incidence rate (per 100K people) 61.8 LK Fürstenfeldbruck, 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 0 500 1000 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 Fürstenfeldbruck cases daily growth factor Germany-LK Fürstenfeldbruck cases daily growth factor (rolling mean) Germany-LK Fürstenfeldbruck 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 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Fürstenfeldbruck deaths daily growth factor Germany-LK Fürstenfeldbruck deaths daily growth factor (rolling mean) Germany-LK Fürstenfeldbruck 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 5000 10000 cases doubling time [days] Germany-LK Fürstenfeldbruck doubling time cases (rolling mean) Germany-LK Fürstenfeldbruck doubling time deaths (rolling mean) 0 1093 2186 daily change Germany-LK Fürstenfeldbruck new cases (rolling 7d mean) Germany-LK Fürstenfeldbruck new cases 0.00 2.19 4.37 6.56 8.74 daily change Germany-LK Fürstenfeldbruck new deaths (rolling 7d mean) Germany-LK Fürstenfeldbruck new deaths 0.0 97.1 194.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Fürstenfeldbruck", dates="2020-03-15:");
2023-03-07T10:50:40.047120 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 Fürstenfeldbruck LK Fürstenfeldbruck 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 Fürstenfeldbruck Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Fürstenfeldbruck")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Fürstenfeldbruck")
print(f'Population of country="Germany", subregion="LK Fürstenfeldbruck": {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 Fürstenfeldbruck": 218579 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 108525 23 270 0
2023-03-03 108502 11 270 0
2023-03-02 108491 19 270 1
2023-03-01 108472 43 269 0
2023-02-28 108429 39 269 1
2023-02-27 108390 61 268 0
2023-02-24 108329 31 268 0
2023-02-23 108298 37 268 0
2023-02-22 108261 94 268 1
2023-02-20 108167 80 267 0
2023-02-17 108087 37 267 0
2023-02-16 108050 50 267 0
2023-02-15 108000 54 267 0
2023-02-14 107946 34 267 0
2023-02-13 107912 92 267 0
2023-02-10 107820 30 267 0
2023-02-09 107790 43 267 0
2023-02-08 107747 40 267 0
2023-02-07 107707 35 267 0
2023-02-06 107672 93 267 0
2023-02-03 107579 27 267 0
2023-02-02 107552 55 267 0
2023-02-01 107497 55 267 0
2023-01-31 107442 39 267 0
2023-01-30 107403 72 267 1
2023-01-28 107331 1 266 0
2023-01-27 107330 28 266 0
2023-01-26 107302 36 266 0
2023-01-25 107266 21 266 0
2023-01-24 107245 47 266 0
2023-01-23 107198 68 266 0
2023-01-20 107130 25 266 0
2023-01-19 107105 21 266 0
2023-01-18 107084 34 266 0
2023-01-17 107050 34 266 0
2023-01-16 107016 57 266 1
2023-01-15 106959 1 265 0
2023-01-14 106958 1 265 0
2023-01-13 106957 3 265 0
2023-01-12 106954 36 265 0
2023-01-11 106918 38 265 0
2023-01-10 106880 38 265 1
2023-01-09 106842 105 264 0
2023-01-08 106737 1 264 0
2023-01-07 106736 1 264 0
2023-01-05 106735 20 264 0
2023-01-04 106715 44 264 0
2023-01-03 106671 30 264 0
2023-01-02 106641 66 264 1
2023-01-01 106575 1 263 0
2022-12-30 106574 41 263 1
2022-12-29 106533 94 262 0
2022-12-28 106439 52 262 1
2022-12-27 106387 119 261 0
2022-12-26 106268 1 261 0
2022-12-24 106267 1 261 0
2022-12-23 106266 43 261 0
2022-12-22 106223 61 261 0
2022-12-21 106162 80 261 0
2022-12-20 106082 41 261 0
2022-12-19 106041 100 261 0
2022-12-16 105941 41 261 0
2022-12-15 105900 64 261 0
2022-12-14 105836 51 261 0
2022-12-13 105785 79 261 0
2022-12-12 105706 101 261 0
2022-12-09 105605 36 261 0
2022-12-08 105569 56 261 0
2022-12-07 105513 44 261 0
2022-12-06 105469 58 261 0
2022-12-05 105411 85 261 0
2022-12-02 105326 39 261 1
2022-12-01 105287 55 260 0
2022-11-30 105232 44 260 0
2022-11-29 105188 50 260 0
2022-11-28 105138 71 260 0
2022-11-26 105067 1 260 0
2022-11-25 105066 38 260 1
2022-11-24 105028 52 259 0
2022-11-23 104976 42 259 0
2022-11-22 104934 24 259 0
2022-11-21 104910 62 259 1
2022-11-18 104848 37 258 0
2022-11-17 104811 28 258 0
2022-11-16 104783 45 258 0
2022-11-15 104738 45 258 0
2022-11-14 104693 103 258 0
2022-11-11 104590 25 258 0
2022-11-10 104565 74 258 0
2022-11-09 104491 69 258 0
2022-11-08 104422 68 258 1
2022-11-07 104354 114 257 0
2022-11-05 104240 2 257 0
2022-11-04 104238 85 257 0
2022-11-03 104153 31 257 1
2022-11-02 104122 139 256 1
2022-10-31 103983 129 255 0
2022-10-29 103854 2 255 0
2022-10-28 103852 86 255 0
2022-10-27 103766 76 255 0
2022-10-26 103690 171 255 0
2022-10-25 103519 82 255 0
2022-10-24 103437 240 255 0
2022-10-22 103197 2 255 0
2022-10-21 103195 149 255 0
2022-10-20 103046 156 255 0
2022-10-19 102890 287 255 1
2022-10-18 102603 224 254 0
2022-10-17 102379 473 254 2
2022-10-14 101906 240 252 0
2022-10-13 101666 324 252 0
2022-10-12 101342 570 252 0
2022-10-11 100772 326 252 1
2022-10-10 100446 998 251 0
2022-10-08 99448 3 251 0
2022-10-07 99445 554 251 2
2022-10-06 98891 811 249 1
2022-10-05 98080 551 248 0
2022-10-04 97529 1147 248 0
2022-10-03 96382 3 248 0
2022-10-01 96379 1 248 0
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2022-09-29 95956 537 248 0
2022-09-28 95419 481 248 1
2022-09-27 94938 355 247 1
2022-09-26 94583 441 246 0
2022-09-25 94142 1 246 0
2022-09-24 94141 2 246 0
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2022-09-22 93970 160 246 2
2022-09-21 93810 139 244 0
2022-09-20 93671 148 244 0
2022-09-19 93523 184 244 0
2022-09-16 93339 85 244 1
2022-09-15 93254 127 243 0
2022-09-14 93127 96 243 1
2022-09-13 93031 108 242 0
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2022-09-09 92774 61 242 0
2022-09-08 92713 80 242 0
2022-09-07 92633 77 242 0
2022-09-06 92556 91 242 0
2022-09-05 92465 150 242 2
2022-09-02 92315 72 240 0
2022-09-01 92243 107 240 1
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2022-08-30 92030 89 239 0
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2022-08-26 91752 45 238 0
2022-08-25 91707 121 238 1
2022-08-24 91586 79 237 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: 0:03:14.246117