Germany: LK Kronach (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 10:55:20 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Kronach", weeks=5);
2023-03-07T10:55:37.005107 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 160 160 7-day incidence rate (per 100K people) 81.7 LK Kronach, 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 0.5 1.0 1.5 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 Kronach cases daily growth factor Germany-LK Kronach cases daily growth factor (rolling mean) Germany-LK Kronach 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 Kronach deaths daily growth factor Germany-LK Kronach deaths daily growth factor (rolling mean) Germany-LK Kronach 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 Kronach doubling time cases (rolling mean) 0.00 6.61 13.22 19.83 daily change Germany-LK Kronach new cases (rolling 7d mean) Germany-LK Kronach new cases 0.000 0.330 0.661 0.991 daily change Germany-LK Kronach new deaths (rolling 7d mean) Germany-LK Kronach new deaths 0.000 0.390 0.779
In [4]:
overview(country="Germany", subregion="LK Kronach");
2023-03-07T10:55:53.656856 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 3000 3000 7-day incidence rate (per 100K people) 81.7 LK Kronach, 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 0 250 500 750 1000 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Kronach cases daily growth factor Germany-LK Kronach cases daily growth factor (rolling mean) Germany-LK Kronach 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 Kronach deaths daily growth factor Germany-LK Kronach deaths daily growth factor (rolling mean) Germany-LK Kronach 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 Kronach doubling time cases (rolling mean) Germany-LK Kronach doubling time deaths (rolling mean) 0.0 165.2 330.5 495.7 660.9 daily change Germany-LK Kronach new cases (rolling 7d mean) Germany-LK Kronach new cases 0.000 1.322 2.644 3.965 5.287 daily change Germany-LK Kronach new deaths (rolling 7d mean) Germany-LK Kronach new deaths 0.0 45.7 91.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Kronach", dates="2020-03-15:");
2023-03-07T10:58:23.467282 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 Kronach LK Kronach 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 Kronach Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Kronach")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Kronach")
print(f'Population of country="Germany", subregion="LK Kronach": {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 Kronach": 66091 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 36409 2 204 0
2023-03-05 36407 1 204 0
2023-03-04 36406 2 204 0
2023-03-03 36404 7 204 0
2023-03-02 36397 11 204 0
2023-03-01 36386 10 204 0
2023-02-28 36376 21 204 0
2023-02-27 36355 14 204 0
2023-02-26 36341 2 204 0
2023-02-25 36339 7 204 0
2023-02-24 36332 11 204 1
2023-02-23 36321 14 203 0
2023-02-22 36307 18 203 0
2023-02-21 36289 20 203 0
2023-02-20 36269 12 203 0
2023-02-19 36257 8 203 0
2023-02-18 36249 7 203 0
2023-02-17 36242 19 203 0
2023-02-16 36223 14 203 0
2023-02-15 36209 15 203 0
2023-02-14 36194 25 203 0
2023-02-13 36169 17 203 0
2023-02-12 36152 6 203 0
2023-02-11 36146 5 203 0
2023-02-10 36141 13 203 0
2023-02-09 36128 10 203 0
2023-02-08 36118 16 203 0
2023-02-07 36102 15 203 0
2023-02-06 36087 14 203 0
2023-02-05 36073 5 203 0
2023-02-04 36068 3 203 1
2023-02-03 36065 14 202 0
2023-02-02 36051 9 202 1
2023-02-01 36042 15 201 0
2023-01-31 36027 21 201 0
2023-01-30 36006 17 201 0
2023-01-29 35989 4 201 1
2023-01-28 35985 3 200 0
2023-01-27 35982 10 200 0
2023-01-26 35972 6 200 0
2023-01-25 35966 11 200 0
2023-01-24 35955 9 200 0
2023-01-23 35946 16 200 0
2023-01-22 35930 1 200 0
2023-01-21 35929 2 200 0
2023-01-20 35927 11 200 0
2023-01-19 35916 8 200 0
2023-01-18 35908 7 200 0
2023-01-17 35901 13 200 0
2023-01-16 35888 10 200 1
2023-01-15 35878 2 199 0
2023-01-14 35876 2 199 0
2023-01-13 35874 9 199 0
2023-01-12 35865 8 199 0
2023-01-11 35857 18 199 1
2023-01-10 35839 19 198 0
2023-01-09 35820 19 198 1
2023-01-08 35801 1 197 0
2023-01-07 35800 3 197 0
2023-01-06 35797 6 197 0
2023-01-05 35791 8 197 0
2023-01-04 35783 14 197 0
2023-01-03 35769 25 197 1
2023-01-02 35744 15 196 1
2022-12-31 35729 4 195 0
2022-12-30 35725 19 195 0
2022-12-29 35706 10 195 0
2022-12-28 35696 15 195 0
2022-12-27 35681 16 195 0
2022-12-26 35665 2 195 0
2022-12-25 35663 1 195 0
2022-12-24 35662 8 195 0
2022-12-23 35654 11 195 0
2022-12-22 35643 17 195 0
2022-12-21 35626 13 195 0
2022-12-20 35613 9 195 0
2022-12-19 35604 13 195 0
2022-12-18 35591 1 195 0
2022-12-17 35590 2 195 0
2022-12-16 35588 13 195 0
2022-12-15 35575 9 195 0
2022-12-14 35566 7 195 0
2022-12-13 35559 15 195 0
2022-12-12 35544 7 195 0
2022-12-11 35537 1 195 0
2022-12-10 35536 5 195 0
2022-12-09 35531 9 195 0
2022-12-08 35522 10 195 0
2022-12-07 35512 14 195 0
2022-12-06 35498 10 195 0
2022-12-05 35488 20 195 0
2022-12-04 35468 1 195 0
2022-12-03 35467 2 195 0
2022-12-02 35465 6 195 0
2022-12-01 35459 5 195 0
2022-11-30 35454 11 195 0
2022-11-29 35443 10 195 0
2022-11-28 35433 5 195 0
2022-11-27 35428 1 195 0
2022-11-26 35427 4 195 0
2022-11-25 35423 8 195 0
2022-11-24 35415 13 195 0
2022-11-23 35402 18 195 0
2022-11-22 35384 13 195 0
2022-11-21 35371 11 195 1
2022-11-20 35360 1 194 0
2022-11-19 35359 1 194 0
2022-11-18 35358 5 194 0
2022-11-17 35353 12 194 0
2022-11-16 35341 11 194 0
2022-11-15 35330 21 194 0
2022-11-14 35309 11 194 0
2022-11-13 35298 1 194 0
2022-11-12 35297 12 194 0
2022-11-11 35285 20 194 0
2022-11-10 35265 15 194 0
2022-11-09 35250 25 194 0
2022-11-08 35225 14 194 0
2022-11-07 35211 33 194 0
2022-11-06 35178 13 194 0
2022-11-05 35165 13 194 1
2022-11-04 35152 29 193 1
2022-11-03 35123 33 192 0
2022-11-02 35090 58 192 0
2022-11-01 35032 18 192 0
2022-10-31 35014 44 192 0
2022-10-30 34970 1 192 0
2022-10-29 34969 17 192 0
2022-10-28 34952 29 192 0
2022-10-27 34923 41 192 1
2022-10-26 34882 55 191 1
2022-10-25 34827 78 190 0
2022-10-24 34749 76 190 0
2022-10-23 34673 21 190 1
2022-10-22 34652 34 189 1
2022-10-21 34618 81 188 0
2022-10-20 34537 40 188 0
2022-10-19 34497 56 188 0
2022-10-18 34441 139 188 1
2022-10-17 34302 93 187 0
2022-10-16 34209 6 187 0
2022-10-15 34203 15 187 1
2022-10-14 34188 67 186 0
2022-10-13 34121 93 186 2
2022-10-12 34028 86 184 1
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2022-10-10 33823 86 182 0
2022-10-09 33737 27 182 0
2022-10-08 33710 40 182 0
2022-10-07 33670 68 182 0
2022-10-06 33602 57 182 0
2022-10-05 33545 125 182 1
2022-10-04 33420 81 181 0
2022-10-03 33339 23 181 0
2022-10-02 33316 2 181 0
2022-10-01 33314 29 181 0
2022-09-30 33285 47 181 0
2022-09-29 33238 35 181 0
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2022-07-24 29917 3 176 0
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2021-01-03 1562 1 36 0
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2020-12-29 1476 15 34 0
2020-12-28 1461 2 34 0
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2020-04-21 123 6 3 1
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2020-03-23 21 5 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: 0:03:20.664936