Germany: LK Helmstedt (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 13:14:59 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Helmstedt", weeks=5);
2023-03-07T13:15:14.406025 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 7-day incidence rate (per 100K people) 55.8 LK Helmstedt, 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 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 Helmstedt cases daily growth factor Germany-LK Helmstedt cases daily growth factor (rolling mean) Germany-LK Helmstedt 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 Helmstedt deaths daily growth factor Germany-LK Helmstedt deaths daily growth factor (rolling mean) Germany-LK Helmstedt 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 Helmstedt doubling time cases (rolling mean) 0.00 9.14 18.28 daily change Germany-LK Helmstedt new cases (rolling 7d mean) Germany-LK Helmstedt new cases 0.000 0.228 0.457 0.685 0.914 daily change Germany-LK Helmstedt new deaths (rolling 7d mean) Germany-LK Helmstedt new deaths 0.000 0.306 0.613 0.919
In [4]:
overview(country="Germany", subregion="LK Helmstedt");
2023-03-07T13:15:32.855529 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) 55.8 LK Helmstedt, 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 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Helmstedt cases daily growth factor Germany-LK Helmstedt cases daily growth factor (rolling mean) Germany-LK Helmstedt 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 Helmstedt deaths daily growth factor Germany-LK Helmstedt deaths daily growth factor (rolling mean) Germany-LK Helmstedt 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 Helmstedt doubling time cases (rolling mean) Germany-LK Helmstedt doubling time deaths (rolling mean) 0.0 182.8 365.5 548.3 daily change Germany-LK Helmstedt new cases (rolling 7d mean) Germany-LK Helmstedt new cases 0.000 1.828 3.655 5.483 daily change Germany-LK Helmstedt new deaths (rolling 7d mean) Germany-LK Helmstedt new deaths 0.0 40.6 81.3 121.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Helmstedt", dates="2020-03-15:");
2023-03-07T13:17:57.625343 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 Helmstedt LK Helmstedt 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 Helmstedt Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Helmstedt")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Helmstedt")
print(f'Population of country="Germany", subregion="LK Helmstedt": {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 Helmstedt": 91379 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 40943 6 179 0
2023-03-03 40937 4 179 0
2023-03-02 40933 3 179 0
2023-03-01 40930 19 179 0
2023-02-28 40911 19 179 0
2023-02-27 40892 14 179 0
2023-02-24 40878 16 179 0
2023-02-23 40862 8 179 0
2023-02-22 40854 13 179 0
2023-02-21 40841 17 179 0
2023-02-20 40824 23 179 0
2023-02-17 40801 14 179 0
2023-02-16 40787 19 179 1
2023-02-15 40768 9 178 0
2023-02-14 40759 22 178 0
2023-02-13 40737 18 178 0
2023-02-10 40719 10 178 0
2023-02-09 40709 6 178 0
2023-02-08 40703 21 178 0
2023-02-07 40682 13 178 0
2023-02-06 40669 13 178 0
2023-02-03 40656 11 178 0
2023-02-02 40645 7 178 0
2023-02-01 40638 19 178 0
2023-01-31 40619 26 178 0
2023-01-30 40593 22 178 0
2023-01-27 40571 10 178 0
2023-01-26 40561 25 178 0
2023-01-25 40536 26 178 0
2023-01-24 40510 29 178 0
2023-01-23 40481 27 178 0
2023-01-20 40454 12 178 0
2023-01-19 40442 12 178 0
2023-01-18 40430 15 178 0
2023-01-17 40415 11 178 0
2023-01-16 40404 23 178 1
2023-01-15 40381 1 177 0
2023-01-14 40380 15 177 0
2023-01-13 40365 19 177 0
2023-01-12 40346 19 177 0
2023-01-11 40327 26 177 0
2023-01-10 40301 35 177 0
2023-01-09 40266 20 177 1
2023-01-08 40246 7 176 0
2023-01-07 40239 14 176 0
2023-01-06 40225 22 176 0
2023-01-05 40203 30 176 0
2023-01-04 40173 31 176 1
2023-01-03 40142 65 175 0
2023-01-02 40077 29 175 0
2023-01-01 40048 6 175 0
2022-12-31 40042 17 175 0
2022-12-30 40025 35 175 0
2022-12-29 39990 44 175 0
2022-12-28 39946 38 175 0
2022-12-27 39908 43 175 0
2022-12-26 39865 8 175 0
2022-12-25 39857 22 175 0
2022-12-24 39835 40 175 0
2022-12-23 39795 79 175 0
2022-12-22 39716 57 175 0
2022-12-21 39659 64 175 0
2022-12-20 39595 103 175 0
2022-12-19 39492 60 175 0
2022-12-18 39432 13 175 0
2022-12-17 39419 36 175 1
2022-12-16 39383 68 174 0
2022-12-15 39315 65 174 0
2022-12-14 39250 44 174 1
2022-12-13 39206 123 173 0
2022-12-12 39083 49 173 0
2022-12-11 39034 7 173 0
2022-12-10 39027 54 173 0
2022-12-09 38973 74 173 0
2022-12-08 38899 65 173 0
2022-12-07 38834 93 173 0
2022-12-06 38741 137 173 0
2022-12-05 38604 23 173 0
2022-12-04 38581 17 173 0
2022-12-03 38564 22 173 0
2022-12-02 38542 55 173 0
2022-12-01 38487 60 173 1
2022-11-30 38427 56 172 0
2022-11-29 38371 109 172 0
2022-11-28 38262 25 172 0
2022-11-27 38237 7 172 0
2022-11-26 38230 54 172 0
2022-11-25 38176 22 172 0
2022-11-24 38154 47 172 1
2022-11-23 38107 59 171 0
2022-11-22 38048 70 171 0
2022-11-21 37978 33 171 0
2022-11-20 37945 3 171 0
2022-11-19 37942 26 171 0
2022-11-18 37916 42 171 0
2022-11-17 37874 48 171 0
2022-11-16 37826 31 171 0
2022-11-15 37795 70 171 0
2022-11-14 37725 45 171 0
2022-11-13 37680 7 171 0
2022-11-12 37673 16 171 0
2022-11-11 37657 46 171 0
2022-11-10 37611 53 171 0
2022-11-09 37558 47 171 0
2022-11-08 37511 71 171 0
2022-11-07 37440 56 171 1
2022-11-06 37384 7 170 0
2022-11-05 37377 29 170 0
2022-11-04 37348 64 170 0
2022-11-03 37284 85 170 1
2022-11-02 37199 125 169 0
2022-11-01 37074 82 169 0
2022-10-31 36992 14 169 0
2022-10-30 36978 13 169 0
2022-10-29 36965 58 169 0
2022-10-28 36907 64 169 1
2022-10-27 36843 81 168 0
2022-10-26 36762 70 168 0
2022-10-25 36692 147 168 0
2022-10-24 36545 105 168 0
2022-10-23 36440 11 168 0
2022-10-22 36429 50 168 0
2022-10-21 36379 105 168 0
2022-10-20 36274 137 168 0
2022-10-19 36137 84 168 0
2022-10-18 36053 198 168 0
2022-10-17 35855 120 168 1
2022-10-16 35735 12 167 1
2022-10-15 35723 50 166 1
2022-10-14 35673 146 165 0
2022-10-13 35527 120 165 0
2022-10-12 35407 159 165 0
2022-10-11 35248 173 165 1
2022-10-10 35075 130 164 0
2022-10-09 34945 19 164 0
2022-10-08 34926 63 164 0
2022-10-07 34863 168 164 0
2022-10-06 34695 167 164 1
2022-10-05 34528 173 163 0
2022-10-04 34355 158 163 0
2022-10-03 34197 20 163 0
2022-10-02 34177 32 163 0
2022-10-01 34145 48 163 0
2022-09-30 34097 130 163 1
2022-09-29 33967 87 162 0
2022-09-28 33880 91 162 0
2022-09-27 33789 91 162 0
2022-09-26 33698 97 162 1
2022-09-25 33601 17 161 0
2022-09-24 33584 25 161 0
2022-09-23 33559 98 161 0
2022-09-22 33461 46 161 0
2022-09-21 33415 102 161 0
2022-09-20 33313 50 161 0
2022-09-19 33263 76 161 0
2022-09-18 33187 10 161 0
2022-09-17 33177 14 161 0
2022-09-16 33163 69 161 0
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2021-02-15 1495 4 71 0
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2021-02-13 1484 10 70 1
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2021-02-09 1427 12 66 2
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2021-02-05 1392 13 63 2
2021-02-04 1379 15 61 1
2021-02-03 1364 14 60 2
2021-02-02 1350 13 58 0
2021-02-01 1337 5 58 1
2021-01-30 1332 12 57 0
2021-01-29 1320 15 57 0
2021-01-28 1305 27 57 0
2021-01-27 1278 33 57 0
2021-01-26 1245 14 57 1
2021-01-25 1231 5 56 1
2021-01-23 1226 19 55 1
2021-01-22 1207 20 54 0
2021-01-21 1187 23 54 0
2021-01-20 1164 26 54 2
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2021-01-18 1096 7 50 1
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2021-01-15 1065 24 49 2
2021-01-14 1041 21 47 0
2021-01-13 1020 30 47 5
2021-01-12 990 37 42 0
2021-01-11 953 1 42 0
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2021-01-06 864 36 38 0
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2021-01-04 790 3 36 2
2021-01-02 787 6 34 1
2020-12-31 781 21 33 2
2020-12-30 760 42 31 2
2020-12-29 718 39 29 4
2020-12-28 679 19 25 6
2020-12-26 660 1 19 0
2020-12-25 659 4 19 0
2020-12-24 655 11 19 1
2020-12-23 644 23 18 7
2020-12-22 621 10 11 1
2020-12-21 611 3 10 0
2020-12-19 608 11 10 1
2020-12-18 597 15 9 2
2020-12-17 582 11 7 0
2020-12-16 571 20 7 2
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2020-11-10 298 8 2 0
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2020-11-05 268 6 2 0
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2020-10-27 214 5 2 0
2020-10-26 209 1 2 0
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2020-10-23 203 2 2 0
2020-10-22 201 2 2 0
2020-10-21 199 1 2 0
2020-10-20 198 5 2 0
2020-10-19 193 5 2 0
2020-10-17 188 3 2 0
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2020-10-13 180 3 2 0
2020-10-12 177 1 2 0
2020-10-10 176 1 2 0
2020-10-09 175 2 2 0
2020-09-30 173 2 2 0
2020-09-28 171 2 2 0
2020-09-23 169 1 2 0
2020-09-22 168 1 2 0
2020-09-21 167 1 2 0
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2020-08-27 158 2 2 0
2020-08-26 156 1 2 0
2020-08-21 155 1 2 0
2020-08-18 154 1 2 0
2020-08-17 153 3 2 1
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2020-05-09 137 1 1 0
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2020-04-27 132 1 1 0
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2020-04-23 128 15 1 0
2020-04-21 113 2 1 0
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2020-04-19 110 20 1 0
2020-04-10 90 2 1 0
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2020-04-06 85 2 1 1
2020-04-04 83 3 0 0
2020-04-03 80 3 0 0
2020-04-01 77 10 0 0
2020-03-31 67 7 0 0
2020-03-30 60 6 0 0
2020-03-29 54 2 0 0
2020-03-28 52 7 0 0
2020-03-27 45 6 0 0
2020-03-26 39 11 0 0
2020-03-25 28 4 0 0
2020-03-24 24 4 0 0
2020-03-23 20 2 0 0
2020-03-22 18 2 0 0
2020-03-21 16 5 0 0
2020-03-20 11 3 0 0
2020-03-19 8 1 0 0
2020-03-18 7 5 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: 0:03:26.639526