Germany: LK Wesermarsch (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 13:38:40 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Wesermarsch", weeks=5);
2023-03-07T13:38:55.757813 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) 48.6 LK Wesermarsch, 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Wesermarsch cases daily growth factor Germany-LK Wesermarsch cases daily growth factor (rolling mean) Germany-LK Wesermarsch estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 R & growth factor (based on deaths) Germany-LK Wesermarsch deaths daily growth factor Germany-LK Wesermarsch deaths daily growth factor (rolling mean) Germany-LK Wesermarsch 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 Wesermarsch doubling time cases (rolling mean) 0.00 8.84 17.69 26.53 daily change Germany-LK Wesermarsch new cases (rolling 7d mean) Germany-LK Wesermarsch new cases 0.000 0.221 0.442 0.663 0.884 daily change Germany-LK Wesermarsch new deaths (rolling 7d mean) Germany-LK Wesermarsch new deaths 0.000 0.267 0.534 0.801
In [4]:
overview(country="Germany", subregion="LK Wesermarsch");
2023-03-07T13:39:12.306927 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) 48.6 LK Wesermarsch, 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 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 R & growth factor (based on cases) Germany-LK Wesermarsch cases daily growth factor Germany-LK Wesermarsch cases daily growth factor (rolling mean) Germany-LK Wesermarsch 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 R & growth factor (based on deaths) Germany-LK Wesermarsch deaths daily growth factor Germany-LK Wesermarsch deaths daily growth factor (rolling mean) Germany-LK Wesermarsch 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 Wesermarsch doubling time cases (rolling mean) Germany-LK Wesermarsch doubling time deaths (rolling mean) 0.0 88.4 176.9 265.3 353.7 daily change Germany-LK Wesermarsch new cases (rolling 7d mean) Germany-LK Wesermarsch new cases 0.000 1.769 3.537 5.306 daily change Germany-LK Wesermarsch new deaths (rolling 7d mean) Germany-LK Wesermarsch new deaths 0.0 33.9 67.7 101.6 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Wesermarsch", dates="2020-03-15:");
2023-03-07T13:41:09.332067 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 Wesermarsch LK Wesermarsch 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 Wesermarsch Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Wesermarsch")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Wesermarsch")
print(f'Population of country="Germany", subregion="LK Wesermarsch": {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 Wesermarsch": 88430 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 39479 5 198 0
2023-03-03 39474 5 198 0
2023-03-02 39469 6 198 0
2023-03-01 39463 6 198 0
2023-02-28 39457 21 198 0
2023-02-27 39436 7 198 0
2023-02-26 39429 1 198 0
2023-02-25 39428 11 198 0
2023-02-24 39417 14 198 0
2023-02-23 39403 17 198 0
2023-02-22 39386 19 198 0
2023-02-21 39367 13 198 0
2023-02-20 39354 6 198 0
2023-02-19 39348 2 198 0
2023-02-18 39346 1 198 0
2023-02-17 39345 12 198 1
2023-02-16 39333 17 197 0
2023-02-15 39316 9 197 0
2023-02-14 39307 15 197 0
2023-02-13 39292 12 197 1
2023-02-12 39280 1 196 0
2023-02-11 39279 10 196 0
2023-02-10 39269 6 196 0
2023-02-09 39263 5 196 0
2023-02-08 39258 11 196 0
2023-02-07 39247 18 196 0
2023-02-06 39229 11 196 0
2023-02-05 39218 4 196 0
2023-02-04 39214 5 196 0
2023-02-03 39209 6 196 0
2023-02-02 39203 13 196 0
2023-02-01 39190 19 196 0
2023-01-31 39171 29 196 0
2023-01-30 39142 12 196 0
2023-01-29 39130 2 196 0
2023-01-28 39128 7 196 1
2023-01-27 39121 17 195 0
2023-01-26 39104 23 195 0
2023-01-25 39081 15 195 0
2023-01-24 39066 23 195 0
2023-01-23 39043 19 195 0
2023-01-22 39024 6 195 0
2023-01-21 39018 11 195 0
2023-01-20 39007 22 195 0
2023-01-19 38985 23 195 0
2023-01-18 38962 17 195 0
2023-01-17 38945 30 195 0
2023-01-16 38915 15 195 2
2023-01-15 38900 10 193 0
2023-01-14 38890 9 193 0
2023-01-13 38881 20 193 1
2023-01-12 38861 21 192 0
2023-01-11 38840 17 192 0
2023-01-10 38823 42 192 0
2023-01-09 38781 23 192 2
2023-01-08 38758 6 190 0
2023-01-07 38752 11 190 0
2023-01-06 38741 13 190 0
2023-01-05 38728 35 190 1
2023-01-04 38693 26 189 0
2023-01-03 38667 64 189 0
2023-01-02 38603 18 189 0
2023-01-01 38585 13 189 0
2022-12-31 38572 18 189 0
2022-12-30 38554 47 189 0
2022-12-29 38507 50 189 1
2022-12-28 38457 74 188 0
2022-12-27 38383 28 188 0
2022-12-26 38355 24 188 0
2022-12-25 38331 28 188 0
2022-12-24 38303 17 188 0
2022-12-23 38286 75 188 2
2022-12-22 38211 64 186 1
2022-12-21 38147 57 185 0
2022-12-20 38090 118 185 0
2022-12-19 37972 49 185 0
2022-12-18 37923 29 185 0
2022-12-17 37894 28 185 0
2022-12-16 37866 66 185 0
2022-12-15 37800 58 185 1
2022-12-14 37742 63 184 0
2022-12-13 37679 147 184 0
2022-12-12 37532 25 184 0
2022-12-11 37507 24 184 0
2022-12-10 37483 67 184 0
2022-12-09 37416 71 184 0
2022-12-08 37345 59 184 0
2022-12-07 37286 98 184 0
2022-12-06 37188 81 184 0
2022-12-05 37107 33 184 0
2022-12-04 37074 23 184 0
2022-12-03 37051 43 184 0
2022-12-02 37008 59 184 0
2022-12-01 36949 64 184 0
2022-11-30 36885 70 184 0
2022-11-29 36815 106 184 0
2022-11-28 36709 45 184 0
2022-11-27 36664 5 184 1
2022-11-26 36659 45 183 0
2022-11-25 36614 56 183 0
2022-11-24 36558 87 183 0
2022-11-23 36471 47 183 0
2022-11-22 36424 97 183 0
2022-11-21 36327 41 183 0
2022-11-20 36286 10 183 0
2022-11-19 36276 29 183 0
2022-11-18 36247 60 183 0
2022-11-17 36187 50 183 0
2022-11-16 36137 53 183 0
2022-11-15 36084 76 183 0
2022-11-14 36008 25 183 0
2022-11-13 35983 6 183 0
2022-11-12 35977 44 183 1
2022-11-11 35933 29 182 0
2022-11-10 35904 49 182 0
2022-11-09 35855 52 182 0
2022-11-08 35803 49 182 0
2022-11-07 35754 15 182 0
2022-11-06 35739 30 182 0
2022-11-05 35709 26 182 0
2022-11-04 35683 56 182 0
2022-11-03 35627 58 182 0
2022-11-02 35569 96 182 0
2022-11-01 35473 20 182 0
2022-10-31 35453 43 182 0
2022-10-30 35410 19 182 0
2022-10-29 35391 39 182 2
2022-10-28 35352 43 180 0
2022-10-27 35309 55 180 0
2022-10-26 35254 90 180 0
2022-10-25 35164 92 180 0
2022-10-24 35072 32 180 0
2022-10-23 35040 53 180 0
2022-10-22 34987 56 180 1
2022-10-21 34931 97 179 0
2022-10-20 34834 110 179 0
2022-10-19 34724 106 179 1
2022-10-18 34618 162 178 0
2022-10-17 34456 69 178 0
2022-10-16 34387 71 178 0
2022-10-15 34316 68 178 0
2022-10-14 34248 109 178 0
2022-10-13 34139 173 178 3
2022-10-12 33966 123 175 0
2022-10-11 33843 147 175 1
2022-10-10 33696 89 174 1
2022-10-09 33607 34 173 0
2022-10-08 33573 70 173 0
2022-10-07 33503 168 173 0
2022-10-06 33335 152 173 0
2022-10-05 33183 261 173 0
2022-10-04 32922 95 173 0
2022-10-03 32827 30 173 0
2022-10-02 32797 54 173 0
2022-10-01 32743 85 173 0
2022-09-30 32658 133 173 0
2022-09-29 32525 72 173 0
2022-09-28 32453 151 173 0
2022-09-27 32302 139 173 1
2022-09-26 32163 46 172 0
2022-09-25 32117 33 172 0
2022-09-24 32084 42 172 1
2022-09-23 32042 69 171 0
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2022-09-21 31908 75 171 1
2022-09-20 31833 62 170 0
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2021-01-01 942 1 17 0
2020-12-31 941 5 17 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: 0:02:54.671710