Germany: LK Northeim (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 13:23:05 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Northeim", weeks=5);
2023-03-07T13:23:18.810674 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 150 150 200 200 250 250 7-day incidence rate (per 100K people) 111.6 LK Northeim, 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 60 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 daily change normalised per 100K 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Northeim cases daily growth factor Germany-LK Northeim cases daily growth factor (rolling mean) Germany-LK Northeim estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Northeim deaths daily growth factor Germany-LK Northeim deaths daily growth factor (rolling mean) Germany-LK Northeim estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Northeim doubling time cases (rolling mean) 0.0 26.4 52.7 79.1 daily change Germany-LK Northeim new cases (rolling 7d mean) Germany-LK Northeim new cases 0.000 0.264 0.527 0.791 daily change Germany-LK Northeim new deaths (rolling 7d mean) Germany-LK Northeim new deaths 0.000 0.286 0.572 0.858
In [4]:
overview(country="Germany", subregion="LK Northeim");
2023-03-07T13:23:33.315310 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 7-day incidence rate (per 100K people) 111.6 LK Northeim, 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 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Northeim cases daily growth factor Germany-LK Northeim cases daily growth factor (rolling mean) Germany-LK Northeim estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 6 6 R & growth factor (based on deaths) Germany-LK Northeim deaths daily growth factor Germany-LK Northeim deaths daily growth factor (rolling mean) Germany-LK Northeim estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Northeim doubling time cases (rolling mean) Germany-LK Northeim doubling time deaths (rolling mean) 0 264 527 791 daily change Germany-LK Northeim new cases (rolling 7d mean) Germany-LK Northeim new cases 0.00 2.64 5.27 7.91 daily change Germany-LK Northeim new deaths (rolling 7d mean) Germany-LK Northeim new deaths 0.0 61.1 122.1 183.2 244.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Northeim", dates="2020-03-15:");
2023-03-07T13:25:53.598919 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 Northeim LK Northeim 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 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Northeim Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Northeim")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Northeim")
print(f'Population of country="Germany", subregion="LK Northeim": {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 Northeim": 131765 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 63480 24 268 0
2023-03-03 63456 10 268 0
2023-03-02 63446 12 268 0
2023-03-01 63434 54 268 1
2023-02-28 63380 47 267 0
2023-02-27 63333 92 267 0
2023-02-24 63241 40 267 0
2023-02-23 63201 59 267 0
2023-02-22 63142 103 267 0
2023-02-21 63039 34 267 0
2023-02-20 63005 40 267 0
2023-02-17 62965 23 267 0
2023-02-16 62942 52 267 1
2023-02-15 62890 21 266 0
2023-02-14 62869 48 266 0
2023-02-13 62821 48 266 1
2023-02-10 62773 37 265 0
2023-02-09 62736 32 265 0
2023-02-08 62704 28 265 0
2023-02-07 62676 59 265 0
2023-02-06 62617 54 265 0
2023-02-03 62563 24 265 0
2023-02-02 62539 29 265 0
2023-02-01 62510 52 265 1
2023-01-31 62458 69 264 0
2023-01-30 62389 64 264 0
2023-01-27 62325 28 264 0
2023-01-26 62297 40 264 0
2023-01-25 62257 40 264 0
2023-01-24 62217 49 264 0
2023-01-23 62168 34 264 0
2023-01-20 62134 21 264 0
2023-01-19 62113 36 264 0
2023-01-18 62077 50 264 0
2023-01-17 62027 53 264 1
2023-01-16 61974 62 263 0
2023-01-13 61912 14 263 0
2023-01-12 61898 32 263 0
2023-01-11 61866 48 263 1
2023-01-10 61818 106 262 1
2023-01-09 61712 122 261 0
2023-01-06 61590 43 261 1
2023-01-05 61547 60 260 0
2023-01-04 61487 63 260 0
2023-01-03 61424 126 260 0
2023-01-02 61298 184 260 2
2022-12-30 61114 50 258 1
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2022-12-20 60286 101 253 0
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2021-04-07 1803 17 46 2
2021-04-06 1786 3 44 0
2021-04-05 1783 8 44 0
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2021-04-03 1772 6 44 0
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2021-04-01 1756 11 44 0
2021-03-31 1745 10 44 0
2021-03-30 1735 12 44 0
2021-03-29 1723 1 44 1
2021-03-28 1722 1 43 0
2021-03-27 1721 8 43 0
2021-03-26 1713 6 43 0
2021-03-25 1707 15 43 0
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2021-03-16 1616 9 41 0
2021-03-14 1607 4 41 0
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2021-02-02 1340 12 34 0
2021-02-01 1328 2 34 0
2021-01-30 1326 5 34 1
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2021-01-28 1310 6 33 1
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2021-01-23 1282 6 32 0
2021-01-22 1276 11 32 0
2021-01-21 1265 14 32 0
2021-01-20 1251 23 32 2
2021-01-19 1228 15 30 2
2021-01-18 1213 1 28 0
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2021-01-14 1186 20 27 1
2021-01-13 1166 33 26 0
2021-01-12 1133 21 26 1
2021-01-11 1112 6 25 1
2021-01-10 1106 2 24 0
2021-01-09 1104 20 24 0
2021-01-08 1084 17 24 0
2021-01-07 1067 34 24 3
2021-01-06 1033 16 21 1
2021-01-05 1017 14 20 0
2021-01-04 1003 3 20 0
2021-01-03 1000 3 20 0
2021-01-02 997 3 20 0
2021-01-01 994 5 20 0
2020-12-31 989 17 20 0
2020-12-30 972 16 20 0
2020-12-29 956 7 20 0
2020-12-28 949 3 20 0
2020-12-27 946 1 20 0
2020-12-26 945 7 20 0
2020-12-25 938 4 20 0
2020-12-24 934 31 20 1
2020-12-23 903 20 19 0
2020-12-22 883 15 19 0
2020-12-21 868 3 19 1
2020-12-20 865 2 18 0
2020-12-19 863 12 18 0
2020-12-18 851 10 18 1
2020-12-17 841 21 17 1
2020-12-16 820 15 16 1
2020-12-15 805 8 15 0
2020-12-14 797 1 15 0
2020-12-13 796 3 15 0
2020-12-12 793 7 15 1
2020-12-11 786 12 14 0
2020-12-10 774 12 14 2
2020-12-09 762 13 12 0
2020-12-08 749 8 12 0
2020-12-07 741 2 12 0
2020-12-06 739 4 12 0
2020-12-05 735 8 12 0
2020-12-04 727 7 12 0
2020-12-03 720 6 12 0
2020-12-02 714 6 12 0
2020-12-01 708 7 12 0
2020-11-30 701 1 12 0
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2020-11-27 694 9 12 0
2020-11-26 685 8 12 1
2020-11-25 677 28 11 0
2020-11-24 649 19 11 0
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2020-11-21 622 15 11 0
2020-11-20 607 7 11 0
2020-11-19 600 13 11 0
2020-11-18 587 23 11 0
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2020-11-11 506 7 10 0
2020-11-10 499 20 10 0
2020-11-09 479 6 10 0
2020-11-08 473 1 10 0
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2020-11-06 457 15 10 0
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2020-11-01 404 1 7 0
2020-10-31 403 8 7 0
2020-10-30 395 11 7 0
2020-10-29 384 6 7 0
2020-10-28 378 15 7 0
2020-10-27 363 5 7 0
2020-10-26 358 1 7 1
2020-10-25 357 1 6 0
2020-10-24 356 5 6 0
2020-10-23 351 2 6 0
2020-10-22 349 2 6 0
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2020-10-20 340 2 6 0
2020-10-17 338 4 6 0
2020-10-16 334 59 6 0
2020-10-15 275 67 6 0
2020-10-14 208 4 6 0
2020-10-13 204 5 6 0
2020-10-12 199 1 6 0
2020-10-10 198 4 6 0
2020-10-09 194 4 6 0
2020-10-08 190 1 6 0
2020-10-06 189 5 6 0
2020-09-26 184 1 6 0
2020-09-21 183 1 6 0
2020-09-16 182 1 6 0
2020-09-15 181 1 6 1
2020-09-09 180 3 5 0
2020-09-07 177 1 5 0
2020-09-05 176 2 5 0
2020-09-03 174 1 5 0
2020-09-02 173 3 5 0
2020-09-01 170 1 5 0
2020-08-29 169 1 5 0
2020-08-25 168 2 5 0
2020-08-20 166 1 5 0
2020-08-17 165 4 5 0
2020-08-07 161 1 5 0
2020-08-06 160 1 5 0
2020-08-05 159 1 5 0
2020-08-04 158 2 5 0
2020-08-03 156 1 5 0
2020-08-02 155 2 5 0
2020-08-01 153 1 5 0
2020-07-31 152 1 5 0
2020-07-29 151 1 5 0
2020-07-18 150 1 5 0
2020-07-14 149 1 5 0
2020-07-09 148 1 5 0
2020-07-08 147 1 5 0
2020-07-06 146 1 5 0
2020-06-29 145 4 5 0
2020-06-25 141 2 5 0
2020-06-24 139 1 5 0
2020-06-19 138 6 5 0
2020-06-17 132 4 5 0
2020-06-15 128 2 5 0
2020-06-10 126 1 5 0
2020-06-06 125 1 5 0
2020-06-05 124 1 5 0
2020-05-30 123 1 5 0
2020-05-29 122 1 5 1
2020-05-20 121 1 4 0
2020-05-19 120 1 4 0
2020-05-14 119 1 4 0
2020-04-30 118 1 4 0
2020-04-29 117 1 4 0
2020-04-26 116 1 4 0
2020-04-25 115 1 4 0
2020-04-23 114 4 4 0
2020-04-22 110 3 4 1
2020-04-21 107 2 3 0
2020-04-19 105 2 3 0
2020-04-18 103 4 3 0
2020-04-17 99 5 3 0
2020-04-16 94 6 3 0
2020-04-15 88 5 3 1
2020-04-14 83 2 2 1
2020-04-13 81 1 1 0
2020-04-12 80 9 1 0
2020-04-11 71 3 1 0
2020-04-10 68 5 1 0
2020-04-09 63 2 1 0
2020-04-08 61 4 1 0
2020-04-06 57 6 1 0
2020-04-04 51 4 1 0
2020-04-03 47 3 1 0
2020-04-02 44 2 1 0
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2020-03-31 39 1 1 0
2020-03-30 38 4 1 0
2020-03-29 34 2 1 0
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2020-03-26 29 3 1 0
2020-03-24 26 4 1 0
2020-03-23 22 5 1 1
2020-03-20 17 3 0 0
2020-03-19 14 5 0 0
2020-03-18 9 2 0 0
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2020-03-12 4 1 0 0

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: 0:03:37.307421