Germany: LK Ammerland (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 12:59:13 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ammerland", weeks=5);
2023-03-07T12:59:26.277945 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) 59.3 LK Ammerland, 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 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 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 Ammerland cases daily growth factor Germany-LK Ammerland cases daily growth factor (rolling mean) Germany-LK Ammerland estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Ammerland deaths daily growth factor Germany-LK Ammerland deaths daily growth factor (rolling mean) Germany-LK Ammerland estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Ammerland doubling time cases (rolling mean) 0.00 12.65 25.30 37.94 50.59 daily change Germany-LK Ammerland new cases (rolling 7d mean) Germany-LK Ammerland new cases 0.000 0.632 1.265 1.897 daily change Germany-LK Ammerland new deaths (rolling 7d mean) Germany-LK Ammerland new deaths 0.000 0.339 0.677
In [4]:
overview(country="Germany", subregion="LK Ammerland");
2023-03-07T12:59:41.290790 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) 59.3 LK Ammerland, 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 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 Ammerland cases daily growth factor Germany-LK Ammerland cases daily growth factor (rolling mean) Germany-LK Ammerland estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Ammerland deaths daily growth factor Germany-LK Ammerland deaths daily growth factor (rolling mean) Germany-LK Ammerland 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 cases doubling time [days] Germany-LK Ammerland doubling time cases (rolling mean) Germany-LK Ammerland doubling time deaths (rolling mean) 0.0 253.0 505.9 daily change Germany-LK Ammerland new cases (rolling 7d mean) Germany-LK Ammerland new cases 0.00 2.53 5.06 7.59 daily change Germany-LK Ammerland new deaths (rolling 7d mean) Germany-LK Ammerland new deaths 0.0 11.7 23.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ammerland", dates="2020-03-15:");
2023-03-07T13:02:00.984183 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 Ammerland LK Ammerland 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 Ammerland Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ammerland")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ammerland")
print(f'Population of country="Germany", subregion="LK Ammerland": {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 Ammerland": 126475 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 58436 15 110 0
2023-03-03 58421 17 110 0
2023-03-02 58404 6 110 0
2023-03-01 58398 23 110 2
2023-02-28 58375 14 108 0
2023-02-27 58361 29 108 0
2023-02-24 58332 7 108 0
2023-02-23 58325 13 108 0
2023-02-22 58312 25 108 0
2023-02-21 58287 28 108 0
2023-02-20 58259 30 108 0
2023-02-17 58229 18 108 0
2023-02-16 58211 24 108 0
2023-02-15 58187 34 108 0
2023-02-14 58153 14 108 0
2023-02-13 58139 29 108 0
2023-02-10 58110 8 108 0
2023-02-09 58102 52 108 0
2023-02-07 58050 21 108 0
2023-02-06 58029 32 108 0
2023-02-03 57997 17 108 0
2023-02-02 57980 23 108 0
2023-02-01 57957 60 108 0
2023-01-31 57897 35 108 0
2023-01-30 57862 43 108 0
2023-01-27 57819 21 108 0
2023-01-26 57798 21 108 0
2023-01-25 57777 48 108 0
2023-01-24 57729 32 108 0
2023-01-23 57697 36 108 0
2023-01-20 57661 13 108 1
2023-01-19 57648 19 107 1
2023-01-18 57629 22 106 0
2023-01-17 57607 26 106 0
2023-01-16 57581 29 106 0
2023-01-13 57552 20 106 0
2023-01-12 57532 25 106 0
2023-01-11 57507 110 106 0
2023-01-10 57397 29 106 0
2023-01-09 57368 39 106 0
2023-01-06 57329 43 106 0
2023-01-05 57286 28 106 0
2023-01-04 57258 59 106 0
2023-01-03 57199 43 106 0
2023-01-02 57156 67 106 1
2022-12-30 57089 57 105 0
2022-12-29 57032 73 105 0
2022-12-28 56959 45 105 0
2022-12-27 56914 112 105 0
2022-12-23 56802 103 105 0
2022-12-22 56699 97 105 0
2022-12-21 56602 138 105 2
2022-12-20 56464 93 103 0
2022-12-19 56371 140 103 0
2022-12-16 56231 94 103 0
2022-12-15 56137 113 103 0
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2022-12-13 55877 147 103 0
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2022-12-09 55651 74 103 0
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2022-12-07 55471 102 103 1
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2022-12-05 55273 168 102 2
2022-12-02 55105 84 100 0
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2021-01-15 1516 7 31 1
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2021-01-04 1333 5 22 2
2021-01-03 1328 5 20 1
2021-01-02 1323 7 19 0
2021-01-01 1316 17 19 0
2020-12-31 1299 11 19 0
2020-12-30 1288 30 19 1
2020-12-29 1258 4 18 0
2020-12-28 1254 6 18 1
2020-12-27 1248 6 17 0
2020-12-26 1242 6 17 0
2020-12-25 1236 22 17 0
2020-12-24 1214 6 17 0
2020-12-23 1208 36 17 0
2020-12-22 1172 3 17 0
2020-12-21 1169 16 17 2
2020-12-20 1153 16 15 0
2020-12-19 1137 28 15 0
2020-12-18 1109 13 15 0
2020-12-17 1096 28 15 0
2020-12-16 1068 18 15 0
2020-12-15 1050 13 15 1
2020-12-14 1037 17 14 0
2020-12-13 1020 23 14 0
2020-12-12 997 22 14 0
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2020-12-10 967 14 14 1
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2020-12-07 909 28 12 0
2020-12-06 881 18 12 0
2020-12-05 863 16 12 0
2020-12-04 847 10 12 0
2020-12-03 837 35 12 0
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2020-10-29 348 15 6 1
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2020-10-22 279 8 5 0
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2020-09-25 222 2 5 0
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2020-08-26 213 1 5 0
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2020-08-17 209 3 5 0
2020-08-05 206 1 5 0
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2020-08-02 202 1 5 0
2020-07-31 201 1 5 0
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2020-07-22 198 2 5 0
2020-07-21 196 3 5 0
2020-07-06 193 1 5 0
2020-06-29 192 1 5 0
2020-06-22 191 1 5 0
2020-06-06 190 1 5 0
2020-06-04 189 1 5 0
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2020-05-30 184 1 5 0
2020-05-29 183 2 5 0
2020-05-27 181 2 5 0
2020-05-26 179 7 5 0
2020-05-25 172 1 5 0
2020-05-22 171 2 5 0
2020-05-19 169 1 5 0
2020-05-18 168 1 5 0
2020-05-13 167 1 5 0
2020-05-11 166 3 5 0
2020-05-06 163 1 5 0
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2020-04-16 151 6 5 0
2020-04-15 145 7 5 0
2020-04-14 138 1 5 0
2020-04-13 137 2 5 1
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2020-04-09 129 4 4 0
2020-04-08 125 6 4 0
2020-04-07 119 6 4 0
2020-04-06 113 5 4 0
2020-04-05 108 1 4 0
2020-04-04 107 1 4 0
2020-04-03 106 9 4 2
2020-04-02 97 7 2 0
2020-04-01 90 3 2 0
2020-03-31 87 6 2 0
2020-03-30 81 2 2 1
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2020-03-27 65 9 1 0
2020-03-26 56 9 1 0
2020-03-25 47 10 1 0
2020-03-24 37 4 1 1
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2020-03-22 28 4 0 0
2020-03-21 24 3 0 0
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2020-03-19 18 3 0 0
2020-03-17 15 6 0 0
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2020-03-13 6 1 0 0
2020-03-12 5 3 0 0
2020-03-09 2 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: 0:03:38.898612