Germany: LK Lüneburg (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 13:22:52 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Lüneburg", weeks=5);
2023-03-07T13:23:07.960268 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 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 54.0 LK Lüneburg, 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 0.00 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 R & growth factor (based on cases) Germany-LK Lüneburg cases daily growth factor Germany-LK Lüneburg cases daily growth factor (rolling mean) Germany-LK Lüneburg 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 Lüneburg deaths daily growth factor Germany-LK Lüneburg deaths daily growth factor (rolling mean) Germany-LK Lüneburg 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 Lüneburg doubling time cases (rolling mean) 0.00 18.51 37.03 55.54 daily change Germany-LK Lüneburg new cases (rolling 7d mean) Germany-LK Lüneburg new cases 0.000 0.463 0.926 1.388 1.851 daily change Germany-LK Lüneburg new deaths (rolling 7d mean) Germany-LK Lüneburg new deaths 0.000 0.294 0.588 0.881
In [4]:
overview(country="Germany", subregion="LK Lüneburg");
2023-03-07T13:23:26.523988 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) 54.0 LK Lüneburg, 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 0 100 200 300 400 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Lüneburg cases daily growth factor Germany-LK Lüneburg cases daily growth factor (rolling mean) Germany-LK Lüneburg 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 Lüneburg deaths daily growth factor Germany-LK Lüneburg deaths daily growth factor (rolling mean) Germany-LK Lüneburg 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 Lüneburg doubling time cases (rolling mean) Germany-LK Lüneburg doubling time deaths (rolling mean) 0.0 185.1 370.3 555.4 740.5 daily change Germany-LK Lüneburg new cases (rolling 7d mean) Germany-LK Lüneburg new cases 0.000 1.851 3.703 daily change Germany-LK Lüneburg new deaths (rolling 7d mean) Germany-LK Lüneburg new deaths 0.0 64.5 129.0 193.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Lüneburg", dates="2020-03-15:");
2023-03-07T13:25:51.869302 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 Lüneburg LK Lüneburg 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 Lüneburg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Lüneburg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Lüneburg")
print(f'Population of country="Germany", subregion="LK Lüneburg": {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 Lüneburg": 185129 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 85476 6 256 0
2023-03-05 85470 4 256 0
2023-03-04 85466 7 256 0
2023-03-03 85459 16 256 0
2023-03-02 85443 6 256 0
2023-03-01 85437 12 256 0
2023-02-28 85425 49 256 2
2023-02-27 85376 37 254 0
2023-02-26 85339 1 254 0
2023-02-25 85338 11 254 0
2023-02-24 85327 30 254 0
2023-02-23 85297 21 254 0
2023-02-22 85276 33 254 0
2023-02-21 85243 39 254 0
2023-02-20 85204 34 254 0
2023-02-19 85170 4 254 0
2023-02-18 85166 11 254 0
2023-02-17 85155 17 254 0
2023-02-16 85138 32 254 0
2023-02-15 85106 25 254 0
2023-02-14 85081 35 254 0
2023-02-13 85046 40 254 0
2023-02-12 85006 3 254 0
2023-02-11 85003 18 254 0
2023-02-10 84985 26 254 0
2023-02-09 84959 19 254 0
2023-02-08 84940 31 254 1
2023-02-07 84909 32 253 0
2023-02-06 84877 42 253 0
2023-02-05 84835 3 253 0
2023-02-04 84832 5 253 0
2023-02-03 84827 28 253 0
2023-02-02 84799 26 253 0
2023-02-01 84773 24 253 0
2023-01-31 84749 59 253 0
2023-01-30 84690 50 253 0
2023-01-29 84640 2 253 0
2023-01-28 84638 25 253 0
2023-01-27 84613 31 253 0
2023-01-26 84582 43 253 1
2023-01-25 84539 44 252 0
2023-01-24 84495 75 252 0
2023-01-23 84420 57 252 0
2023-01-22 84363 7 252 2
2023-01-21 84356 15 250 0
2023-01-20 84341 33 250 0
2023-01-19 84308 27 250 0
2023-01-18 84281 42 250 0
2023-01-17 84239 45 250 0
2023-01-16 84194 60 250 0
2023-01-15 84134 3 250 0
2023-01-14 84131 17 250 0
2023-01-13 84114 38 250 0
2023-01-12 84076 36 250 0
2023-01-11 84040 72 250 1
2023-01-10 83968 74 249 0
2023-01-09 83894 85 249 0
2023-01-08 83809 5 249 0
2023-01-07 83804 27 249 0
2023-01-06 83777 49 249 0
2023-01-05 83728 90 249 0
2023-01-04 83638 70 249 0
2023-01-03 83568 118 249 1
2023-01-02 83450 111 248 1
2023-01-01 83339 5 247 2
2022-12-31 83334 30 245 2
2022-12-30 83304 94 243 0
2022-12-29 83210 123 243 1
2022-12-28 83087 206 242 0
2022-12-27 82881 130 242 0
2022-12-26 82751 29 242 1
2022-12-25 82722 9 241 0
2022-12-24 82713 36 241 1
2022-12-23 82677 169 240 0
2022-12-22 82508 183 240 4
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2022-12-20 82177 332 236 3
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2022-12-12 80766 242 229 1
2022-12-11 80524 13 228 0
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2022-12-09 80451 136 228 2
2022-12-08 80315 189 226 0
2022-12-07 80126 157 226 1
2022-12-06 79969 209 225 2
2022-12-05 79760 250 223 2
2022-12-04 79510 16 221 0
2022-12-03 79494 88 221 1
2022-12-02 79406 197 220 2
2022-12-01 79209 130 218 0
2022-11-30 79079 199 218 0
2022-11-29 78880 260 218 0
2022-11-28 78620 183 218 0
2022-11-27 78437 8 218 1
2022-11-26 78429 50 217 0
2022-11-25 78379 145 217 2
2022-11-24 78234 124 215 1
2022-11-23 78110 104 214 0
2022-11-22 78006 185 214 1
2022-11-21 77821 147 213 1
2022-11-20 77674 5 212 0
2022-11-19 77669 76 212 1
2022-11-18 77593 61 211 0
2022-11-17 77532 85 211 0
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2022-11-12 77093 44 209 0
2022-11-11 77049 87 209 0
2022-11-10 76962 116 209 0
2022-11-09 76846 115 209 0
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2022-11-07 76622 141 209 1
2022-11-06 76481 2 208 0
2022-11-05 76479 43 208 0
2022-11-04 76436 104 208 1
2022-11-03 76332 168 207 0
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2022-11-01 75962 164 206 0
2022-10-31 75798 56 206 1
2022-10-30 75742 2 205 0
2022-10-29 75740 39 205 1
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2022-10-27 75577 105 203 0
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2022-10-23 74868 24 203 1
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2022-10-17 73805 187 194 1
2022-10-16 73618 13 193 0
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2022-10-14 73492 240 193 1
2022-10-13 73252 212 192 0
2022-10-12 73040 222 192 0
2022-10-11 72818 348 192 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: 0:04:02.501579