Germany: LK Diepholz (Niedersachsen)¶

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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 13:22:49 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Diepholz", weeks=5);
2023-03-07T13:23:02.742610 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 7-day incidence rate (per 100K people) 56.2 LK Diepholz, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Diepholz new deaths (rolling 7d mean) Germany-LK Diepholz new deaths 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 R & growth factor (based on cases) Germany-LK Diepholz cases daily growth factor Germany-LK Diepholz cases daily growth factor (rolling mean) Germany-LK Diepholz estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Diepholz deaths daily growth factor Germany-LK Diepholz deaths daily growth factor (rolling mean) Germany-LK Diepholz 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 Diepholz doubling time cases (rolling mean) 0.00 21.88 43.77 65.65 daily change Germany-LK Diepholz new cases (rolling 7d mean) Germany-LK Diepholz new cases 0.000 0.212 0.425 0.637 0.849
In [4]:
overview(country="Germany", subregion="LK Diepholz");
2023-03-07T13:23:16.023127 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 2000 2000 7-day incidence rate (per 100K people) 56.2 LK Diepholz, 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 1 2 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 R & growth factor (based on cases) Germany-LK Diepholz cases daily growth factor Germany-LK Diepholz cases daily growth factor (rolling mean) Germany-LK Diepholz estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Diepholz deaths daily growth factor Germany-LK Diepholz deaths daily growth factor (rolling mean) Germany-LK Diepholz 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 Diepholz doubling time cases (rolling mean) Germany-LK Diepholz doubling time deaths (rolling mean) 0 438 875 daily change Germany-LK Diepholz new cases (rolling 7d mean) Germany-LK Diepholz new cases 0.000 2.188 4.377 daily change Germany-LK Diepholz new deaths (rolling 7d mean) Germany-LK Diepholz new deaths 0.0 38.4 76.8 115.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Diepholz", dates="2020-03-15:");
2023-03-07T13:25:34.044824 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 Diepholz LK Diepholz 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 Diepholz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Diepholz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Diepholz")
print(f'Population of country="Germany", subregion="LK Diepholz": {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 Diepholz": 218839 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 104700 21 193 0
2023-03-03 104679 16 193 0
2023-03-02 104663 22 193 0
2023-03-01 104641 26 193 0
2023-02-28 104615 38 193 0
2023-02-27 104577 42 193 0
2023-02-24 104535 27 193 0
2023-02-23 104508 41 193 0
2023-02-22 104467 56 193 0
2023-02-21 104411 69 193 0
2023-02-20 104342 51 193 0
2023-02-17 104291 35 193 0
2023-02-16 104256 30 193 0
2023-02-15 104226 35 193 0
2023-02-14 104191 42 193 0
2023-02-13 104149 40 193 0
2023-02-10 104109 25 193 0
2023-02-09 104084 36 193 0
2023-02-08 104048 32 193 0
2023-02-07 104016 52 193 0
2023-02-06 103964 34 193 0
2023-02-03 103930 36 193 0
2023-02-02 103894 29 193 0
2023-02-01 103865 45 193 0
2023-01-31 103820 63 193 0
2023-01-30 103757 66 193 0
2023-01-27 103691 28 193 0
2023-01-26 103663 41 193 0
2023-01-25 103622 47 193 0
2023-01-24 103575 51 193 0
2023-01-23 103524 49 193 0
2023-01-20 103475 20 193 0
2023-01-19 103455 24 193 0
2023-01-18 103431 39 193 0
2023-01-17 103392 45 193 0
2023-01-16 103347 59 193 0
2023-01-13 103288 31 193 0
2023-01-12 103257 33 193 0
2023-01-11 103224 67 193 0
2023-01-10 103157 80 193 0
2023-01-09 103077 150 193 0
2023-01-07 102927 1 193 0
2023-01-05 102926 78 193 0
2023-01-04 102848 120 193 0
2023-01-03 102728 97 193 0
2023-01-02 102631 223 193 0
2022-12-31 102408 3 193 0
2022-12-30 102405 1 193 0
2022-12-29 102404 130 193 0
2022-12-28 102274 207 193 0
2022-12-27 102067 275 193 0
2022-12-24 101792 1 193 0
2022-12-23 101791 116 193 0
2022-12-22 101675 184 193 0
2022-12-21 101491 339 193 0
2022-12-20 101152 189 193 0
2022-12-19 100963 333 193 1
2022-12-16 100630 164 192 1
2022-12-15 100466 215 191 1
2022-12-14 100251 237 190 0
2022-12-13 100014 260 190 0
2022-12-12 99754 275 190 1
2022-12-09 99479 129 189 1
2022-12-08 99350 186 188 0
2022-12-07 99164 233 188 0
2022-12-06 98931 239 188 1
2022-12-05 98692 242 187 0
2022-12-04 98450 1 187 0
2022-12-02 98449 95 187 0
2022-12-01 98354 175 187 0
2022-11-30 98179 168 187 1
2022-11-29 98011 214 186 0
2022-11-28 97797 206 186 1
2022-11-25 97591 87 185 0
2022-11-24 97504 141 185 0
2022-11-23 97363 140 185 0
2022-11-22 97223 161 185 0
2022-11-21 97062 159 185 0
2022-11-18 96903 81 185 0
2022-11-17 96822 87 185 0
2022-11-16 96735 148 185 1
2022-11-15 96587 166 184 1
2022-11-14 96421 164 183 0
2022-11-11 96257 104 183 0
2022-11-10 96153 111 183 0
2022-11-09 96042 147 183 0
2022-11-08 95895 167 183 0
2022-11-07 95728 214 183 0
2022-11-04 95514 107 183 0
2022-11-03 95407 209 183 0
2022-11-02 95198 275 183 1
2022-11-01 94923 313 182 2
2022-10-31 94610 1 180 0
2022-10-28 94609 151 180 0
2022-10-27 94458 163 180 0
2022-10-26 94295 216 180 0
2022-10-25 94079 367 180 0
2022-10-24 93712 310 180 1
2022-10-21 93402 158 179 0
2022-10-20 93244 234 179 0
2022-10-19 93010 240 179 0
2022-10-18 92770 434 179 0
2022-10-17 92336 478 179 1
2022-10-14 91858 197 178 1
2022-10-13 91661 399 177 0
2022-10-12 91262 292 177 0
2022-10-11 90970 530 177 0
2022-10-10 90440 422 177 0
2022-10-07 90018 260 177 0
2022-10-06 89758 346 177 0
2022-10-05 89412 410 177 0
2022-10-04 89002 408 177 1
2022-10-03 88594 2 176 0
2022-09-30 88592 197 176 0
2022-09-29 88395 182 176 0
2022-09-28 88213 237 176 0
2022-09-27 87976 273 176 0
2022-09-26 87703 350 176 0
2022-09-23 87353 118 176 0
2022-09-22 87235 185 176 1
2022-09-21 87050 208 175 1
2022-09-20 86842 264 174 1
2022-09-19 86578 279 173 1
2022-09-16 86299 110 172 0
2022-09-15 86189 139 172 0
2022-09-14 86050 223 172 0
2022-09-13 85827 242 172 0
2022-09-12 85585 212 172 0
2022-09-09 85373 151 172 0
2022-09-08 85222 184 172 1
2022-09-07 85038 230 171 0
2022-09-06 84808 289 171 0
2022-09-05 84519 300 171 1
2022-09-02 84219 107 170 0
2022-09-01 84112 131 170 0
2022-08-31 83981 108 170 0
2022-08-30 83873 149 170 0
2022-08-29 83724 165 170 0
2022-08-26 83559 80 170 0
2022-08-25 83479 93 170 0
2022-08-24 83386 107 170 0
2022-08-23 83279 159 170 0
2022-08-22 83120 200 170 0
2022-08-19 82920 88 170 0
2022-08-18 82832 120 170 0
2022-08-17 82712 122 170 0
2022-08-16 82590 187 170 0
2022-08-15 82403 206 170 0
2022-08-12 82197 127 170 0
2022-08-11 82070 117 170 1
2022-08-10 81953 167 169 0
2022-08-09 81786 208 169 0
2022-08-08 81578 207 169 1
2022-08-05 81371 119 168 0
2022-08-04 81252 154 168 0
2022-08-03 81098 204 168 1
2022-08-02 80894 273 167 0
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2022-06-24 69499 341 159 0
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2022-06-22 68817 405 158 1
2022-06-21 68412 386 157 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: 0:04:05.258520