Germany: LK Kitzingen (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 10:55:42 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Kitzingen", weeks=5);
2023-03-07T10:55:54.548036 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 7-day incidence rate (per 100K people) 83.7 LK Kitzingen, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Kitzingen cases daily growth factor Germany-LK Kitzingen cases daily growth factor (rolling mean) Germany-LK Kitzingen 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 R & growth factor (based on deaths) Germany-LK Kitzingen deaths daily growth factor Germany-LK Kitzingen deaths daily growth factor (rolling mean) Germany-LK Kitzingen 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 Kitzingen doubling time cases (rolling mean) 0.00 18.40 36.79 daily change Germany-LK Kitzingen new cases (rolling 7d mean) Germany-LK Kitzingen new cases 0.00 0.23 0.46 0.69 0.92 daily change Germany-LK Kitzingen new deaths (rolling 7d mean) Germany-LK Kitzingen new deaths 0.000 0.239 0.478 0.717 0.955
In [4]:
overview(country="Germany", subregion="LK Kitzingen");
2023-03-07T10:56:12.367347 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) 83.7 LK Kitzingen, 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 0 200 400 600 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Kitzingen cases daily growth factor Germany-LK Kitzingen cases daily growth factor (rolling mean) Germany-LK Kitzingen 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 R & growth factor (based on deaths) Germany-LK Kitzingen deaths daily growth factor Germany-LK Kitzingen deaths daily growth factor (rolling mean) Germany-LK Kitzingen 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 Kitzingen doubling time cases (rolling mean) Germany-LK Kitzingen doubling time deaths (rolling mean) 0.0 184.0 367.9 551.9 daily change Germany-LK Kitzingen new cases (rolling 7d mean) Germany-LK Kitzingen new cases 0.000 1.840 3.679 5.519 7.358 daily change Germany-LK Kitzingen new deaths (rolling 7d mean) Germany-LK Kitzingen new deaths 0.0 36.2 72.3 108.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Kitzingen", dates="2020-03-15:");
2023-03-07T10:58:27.083564 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 Kitzingen LK Kitzingen 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 Kitzingen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Kitzingen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Kitzingen")
print(f'Population of country="Germany", subregion="LK Kitzingen": {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 Kitzingen": 91980 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 46652 14 189 0
2023-03-03 46638 18 189 0
2023-03-02 46620 10 189 0
2023-03-01 46610 14 189 0
2023-02-28 46596 21 189 0
2023-02-27 46575 46 189 0
2023-02-24 46529 20 189 0
2023-02-23 46509 25 189 0
2023-02-22 46484 18 189 0
2023-02-21 46466 13 189 0
2023-02-20 46453 26 189 0
2023-02-17 46427 21 189 0
2023-02-16 46406 8 189 0
2023-02-15 46398 39 189 0
2023-02-14 46359 17 189 0
2023-02-13 46342 36 189 0
2023-02-10 46306 17 189 0
2023-02-09 46289 11 189 0
2023-02-08 46278 18 189 1
2023-02-07 46260 19 188 0
2023-02-06 46241 16 188 0
2023-02-03 46225 13 188 0
2023-02-02 46212 10 188 0
2023-02-01 46202 14 188 0
2023-01-31 46188 20 188 0
2023-01-30 46168 20 188 0
2023-01-27 46148 10 188 0
2023-01-26 46138 10 188 0
2023-01-25 46128 13 188 0
2023-01-24 46115 18 188 0
2023-01-23 46097 8 188 0
2023-01-20 46089 10 188 0
2023-01-19 46079 11 188 0
2023-01-18 46068 3 188 0
2023-01-17 46065 11 188 0
2023-01-16 46054 14 188 0
2023-01-13 46040 7 188 0
2023-01-12 46033 10 188 0
2023-01-11 46023 10 188 0
2023-01-10 46013 13 188 0
2023-01-09 46000 37 188 1
2023-01-05 45963 18 187 0
2023-01-04 45945 23 187 0
2023-01-03 45922 13 187 0
2023-01-02 45909 33 187 1
2022-12-30 45876 10 186 0
2022-12-29 45866 20 186 1
2022-12-28 45846 27 185 0
2022-12-27 45819 59 185 1
2022-12-23 45760 5 184 0
2022-12-22 45755 50 184 0
2022-12-20 45705 32 184 0
2022-12-19 45673 29 184 1
2022-12-16 45644 25 183 0
2022-12-15 45619 18 183 0
2022-12-14 45601 25 183 0
2022-12-13 45576 20 183 1
2022-12-12 45556 48 182 0
2022-12-09 45508 22 182 0
2022-12-08 45486 19 182 0
2022-12-07 45467 12 182 0
2022-12-06 45455 27 182 0
2022-12-05 45428 36 182 2
2022-12-02 45392 26 180 0
2022-12-01 45366 20 180 0
2022-11-30 45346 20 180 1
2022-11-29 45326 30 179 0
2022-11-28 45296 43 179 0
2022-11-25 45253 16 179 0
2022-11-24 45237 22 179 0
2022-11-23 45215 10 179 0
2022-11-22 45205 18 179 0
2022-11-21 45187 52 179 0
2022-11-18 45135 18 179 0
2022-11-17 45117 23 179 1
2022-11-16 45094 25 178 0
2022-11-15 45069 34 178 0
2022-11-14 45035 67 178 1
2022-11-11 44968 22 177 0
2022-11-10 44946 21 177 0
2022-11-09 44925 35 177 0
2022-11-08 44890 35 177 0
2022-11-07 44855 55 177 0
2022-11-04 44800 30 177 0
2022-11-03 44770 57 177 0
2022-11-02 44713 52 177 0
2022-10-31 44661 90 177 1
2022-10-30 44571 1 176 0
2022-10-28 44570 54 176 0
2022-10-27 44516 47 176 0
2022-10-26 44469 70 176 0
2022-10-25 44399 79 176 0
2022-10-24 44320 152 176 0
2022-10-21 44168 73 176 1
2022-10-20 44095 95 175 1
2022-10-19 44000 124 174 0
2022-10-18 43876 167 174 0
2022-10-17 43709 244 174 2
2022-10-14 43465 108 172 0
2022-10-13 43357 142 172 1
2022-10-12 43215 130 171 0
2022-10-11 43085 165 171 0
2022-10-10 42920 275 171 0
2022-10-08 42645 1 171 0
2022-10-07 42644 150 171 0
2022-10-06 42494 149 171 1
2022-10-05 42345 168 170 0
2022-10-04 42177 218 170 3
2022-09-30 41959 78 167 0
2022-09-29 41881 105 167 2
2022-09-28 41776 79 165 0
2022-09-27 41697 112 165 0
2022-09-26 41585 155 165 0
2022-09-23 41430 22 165 0
2022-09-22 41408 40 165 0
2022-09-21 41368 40 165 0
2022-09-20 41328 75 165 0
2022-09-19 41253 72 165 1
2022-09-16 41181 28 164 0
2022-09-15 41153 23 164 0
2022-09-14 41130 51 164 0
2022-09-13 41079 57 164 0
2022-09-12 41022 52 164 0
2022-09-09 40970 20 164 0
2022-09-08 40950 27 164 0
2022-09-07 40923 34 164 0
2022-09-06 40889 26 164 0
2022-09-05 40863 43 164 0
2022-09-02 40820 27 164 0
2022-09-01 40793 10 164 0
2022-08-31 40783 14 164 0
2022-08-30 40769 52 164 0
2022-08-29 40717 50 164 0
2022-08-26 40667 48 164 1
2022-08-25 40619 23 163 0
2022-08-24 40596 77 163 0
2022-08-23 40519 9 163 0
2022-08-22 40510 66 163 1
2022-08-19 40444 19 162 1
2022-08-18 40425 35 161 0
2022-08-17 40390 65 161 0
2022-08-16 40325 41 161 1
2022-08-15 40284 97 160 0
2022-08-12 40187 50 160 0
2022-08-11 40137 57 160 0
2022-08-10 40080 49 160 1
2022-08-09 40031 86 159 0
2022-08-08 39945 145 159 2
2022-08-07 39800 1 157 0
2022-08-05 39799 66 157 0
2022-08-04 39733 76 157 1
2022-08-03 39657 105 156 1
2022-08-02 39552 124 155 0
2022-08-01 39428 203 155 1
2022-07-31 39225 1 154 0
2022-07-29 39224 216 154 0
2022-07-28 39008 79 154 1
2022-07-27 38929 278 153 0
2022-07-26 38651 178 153 1
2022-07-25 38473 281 152 2
2022-07-22 38192 170 150 2
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2022-07-16 37035 1 148 0
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2022-06-24 34580 96 145 0
2022-06-23 34484 100 145 0
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2022-06-10 33485 13 145 0
2022-06-09 33472 69 145 0
2022-06-08 33403 72 145 0
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2022-06-03 33250 38 145 0
2022-06-02 33212 24 145 0
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2022-05-31 33154 47 145 1
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2022-05-27 33057 23 144 0
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2022-05-24 33001 71 144 1
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2022-05-22 32921 16 143 0
2022-05-21 32905 26 143 0
2022-05-20 32879 31 143 0
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2022-05-12 32540 69 143 0
2022-05-11 32471 56 143 0
2022-05-10 32415 102 143 1
2022-05-09 32313 101 142 0
2022-05-07 32212 73 142 1
2022-05-06 32139 58 141 0
2022-05-05 32081 78 141 0
2022-05-04 32003 128 141 0
2022-05-03 31875 117 141 0
2022-05-02 31758 84 141 0
2022-04-30 31674 59 141 0
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2022-04-28 31499 81 141 0
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2022-04-26 31335 229 141 0
2022-04-25 31106 50 141 0
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2022-04-22 30902 138 140 0
2022-04-21 30764 247 140 0
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2022-04-18 30178 42 140 1
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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: 0:02:58.620815