Germany: LK Birkenfeld (Rheinland-Pfalz)¶

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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 14:40:11 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Birkenfeld", weeks=5);
2023-03-07T14:40:25.393320 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 50 50 100 100 150 150 7-day incidence rate (per 100K people) 79.2 LK Birkenfeld, 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 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 2 2 4 4 6 6 8 8 10 10 R & growth factor (based on cases) Germany-LK Birkenfeld cases daily growth factor Germany-LK Birkenfeld cases daily growth factor (rolling mean) Germany-LK Birkenfeld 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 Birkenfeld deaths daily growth factor Germany-LK Birkenfeld deaths daily growth factor (rolling mean) Germany-LK Birkenfeld estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10000 20000 cases doubling time [days] Germany-LK Birkenfeld doubling time cases (rolling mean) 0.00 16.17 32.34 daily change Germany-LK Birkenfeld new cases (rolling 7d mean) Germany-LK Birkenfeld new cases 0.000 0.202 0.404 0.606 0.808 daily change Germany-LK Birkenfeld new deaths (rolling 7d mean) Germany-LK Birkenfeld new deaths 0.000 0.369 0.738
In [4]:
overview(country="Germany", subregion="LK Birkenfeld");
2023-03-07T14:40:43.348866 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) 79.2 LK Birkenfeld, 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 8 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 2 2 4 4 6 6 8 8 10 10 R & growth factor (based on cases) Germany-LK Birkenfeld cases daily growth factor Germany-LK Birkenfeld cases daily growth factor (rolling mean) Germany-LK Birkenfeld 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 Birkenfeld deaths daily growth factor Germany-LK Birkenfeld deaths daily growth factor (rolling mean) Germany-LK Birkenfeld estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 10000 20000 30000 cases doubling time [days] Germany-LK Birkenfeld doubling time cases (rolling mean) Germany-LK Birkenfeld doubling time deaths (rolling mean) 0.0 161.7 323.4 485.1 daily change Germany-LK Birkenfeld new cases (rolling 7d mean) Germany-LK Birkenfeld new cases 0.000 1.617 3.234 4.851 6.468 daily change Germany-LK Birkenfeld new deaths (rolling 7d mean) Germany-LK Birkenfeld new deaths 0.0 41.1 82.2 123.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Birkenfeld", dates="2020-03-15:");
2023-03-07T14:42:56.483410 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 Birkenfeld LK Birkenfeld 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 Birkenfeld Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Birkenfeld")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Birkenfeld")
print(f'Population of country="Germany", subregion="LK Birkenfeld": {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 Birkenfeld": 80849 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 35667 5 175 0
2023-03-02 35662 5 175 0
2023-03-01 35657 17 175 0
2023-02-28 35640 37 175 0
2023-02-27 35603 43 175 0
2023-02-24 35560 17 175 0
2023-02-23 35543 25 175 0
2023-02-22 35518 16 175 1
2023-02-21 35502 11 174 0
2023-02-20 35491 22 174 0
2023-02-17 35469 17 174 0
2023-02-16 35452 30 174 0
2023-02-15 35422 23 174 0
2023-02-14 35399 43 174 0
2023-02-13 35356 5 174 0
2023-02-10 35351 1 174 0
2023-02-07 35350 1 174 0
2023-02-06 35349 6 174 0
2023-02-03 35343 6 174 0
2023-01-27 35337 6 174 0
2023-01-26 35331 10 174 0
2023-01-25 35321 3 174 0
2023-01-24 35318 20 174 0
2023-01-23 35298 11 174 0
2023-01-20 35287 7 174 0
2023-01-19 35280 8 174 0
2023-01-18 35272 8 174 0
2023-01-17 35264 13 174 0
2023-01-16 35251 16 174 0
2023-01-13 35235 7 174 0
2023-01-12 35228 9 174 0
2023-01-11 35219 6 174 0
2023-01-10 35213 21 174 0
2023-01-09 35192 18 174 0
2023-01-06 35174 13 174 0
2023-01-05 35161 6 174 0
2023-01-04 35155 25 174 1
2023-01-03 35130 35 173 0
2023-01-02 35095 45 173 0
2022-12-30 35050 15 173 0
2022-12-29 35035 38 173 0
2022-12-28 34997 47 173 1
2022-12-27 34950 29 172 0
2022-12-23 34921 20 172 0
2022-12-22 34901 35 172 0
2022-12-21 34866 45 172 0
2022-12-20 34821 44 172 0
2022-12-19 34777 55 172 0
2022-12-16 34722 17 172 0
2022-12-15 34705 33 172 2
2022-12-14 34672 18 170 0
2022-12-13 34654 36 170 0
2022-12-12 34618 54 170 0
2022-12-09 34564 17 170 0
2022-12-08 34547 17 170 0
2022-12-07 34530 31 170 0
2022-12-06 34499 39 170 0
2022-12-05 34460 34 170 0
2022-12-02 34426 17 170 0
2022-12-01 34409 23 170 0
2022-11-30 34386 24 170 0
2022-11-29 34362 23 170 0
2022-11-28 34339 42 170 0
2022-11-25 34297 8 170 0
2022-11-24 34289 26 170 0
2022-11-23 34263 23 170 0
2022-11-22 34240 27 170 0
2022-11-21 34213 45 170 0
2022-11-18 34168 25 170 0
2022-11-17 34143 32 170 0
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2022-11-15 34085 49 170 1
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2022-10-10 31960 228 167 0
2022-10-07 31732 147 167 0
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2022-09-22 30289 76 167 1
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2021-01-06 1399 9 56 0
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2021-01-04 1373 15 55 3
2021-01-03 1358 10 52 0
2021-01-02 1348 9 52 2
2021-01-01 1339 5 50 0
2020-12-31 1334 3 50 1
2020-12-30 1331 6 49 0
2020-12-29 1325 25 49 1
2020-12-28 1300 3 48 0
2020-12-27 1297 2 48 0
2020-12-26 1295 3 48 0
2020-12-25 1292 6 48 0
2020-12-24 1286 5 48 0
2020-12-23 1281 10 48 0
2020-12-22 1271 9 48 0
2020-12-21 1262 15 48 0
2020-12-20 1247 8 48 1
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2020-12-17 1204 25 46 0
2020-12-16 1179 29 46 0
2020-12-15 1150 7 46 0
2020-12-14 1143 5 46 0
2020-12-13 1138 14 46 0
2020-12-12 1124 18 46 2
2020-12-11 1106 24 44 0
2020-12-10 1082 24 44 0
2020-12-09 1058 9 44 0
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2020-12-07 1038 4 44 0
2020-12-06 1034 4 44 0
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2020-11-30 947 30 44 1
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2020-11-25 870 7 42 1
2020-11-24 863 29 41 3
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2020-11-16 743 27 35 1
2020-11-15 716 31 34 3
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2020-11-13 667 10 29 2
2020-11-12 657 44 27 3
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2020-11-01 476 22 14 1
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2020-10-30 441 9 13 0
2020-10-29 432 42 13 2
2020-10-28 390 35 11 1
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2020-10-26 342 12 10 0
2020-10-25 330 16 10 0
2020-10-24 314 10 10 0
2020-10-23 304 17 10 0
2020-10-22 287 13 10 1
2020-10-21 274 14 9 1
2020-10-20 260 24 8 2
2020-10-19 236 2 6 0
2020-10-18 234 4 6 1
2020-10-17 230 8 5 0
2020-10-16 222 27 5 1
2020-10-15 195 52 4 0
2020-10-14 143 2 4 0
2020-10-12 141 2 4 0
2020-10-10 139 2 4 0
2020-10-09 137 1 4 0
2020-10-08 136 4 4 0
2020-10-07 132 3 4 0
2020-10-03 129 1 4 0
2020-10-02 128 2 4 0
2020-10-01 126 1 4 0
2020-09-30 125 1 4 0
2020-09-23 124 1 4 0
2020-09-18 123 1 4 0
2020-09-16 122 1 4 0
2020-09-14 121 1 4 0
2020-09-11 120 1 4 0
2020-09-01 119 2 4 0
2020-08-27 117 3 4 0
2020-08-24 114 1 4 0
2020-08-22 113 8 4 0
2020-08-21 105 2 4 0
2020-08-19 103 2 4 0
2020-08-18 101 1 4 0
2020-08-17 100 2 4 0
2020-08-13 98 4 4 0
2020-08-08 94 1 4 1
2020-07-20 93 1 3 0
2020-07-13 92 1 3 0
2020-07-06 91 1 3 0
2020-06-02 90 1 3 1
2020-05-25 89 1 2 0
2020-05-19 88 1 2 0
2020-05-18 87 2 2 0
2020-05-14 85 1 2 0
2020-05-02 84 1 2 0
2020-04-28 83 1 2 0
2020-04-27 82 1 2 0
2020-04-23 81 2 2 0
2020-04-22 79 1 2 0
2020-04-20 78 1 2 0
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2020-04-18 75 1 2 0
2020-04-17 74 1 2 0
2020-04-16 73 2 2 0
2020-04-14 71 2 2 0
2020-04-12 69 2 2 0
2020-04-10 67 4 2 0
2020-04-09 63 7 2 1
2020-04-07 56 4 1 0
2020-04-06 52 1 1 0
2020-04-04 51 3 1 0
2020-04-03 48 5 1 0
2020-04-02 43 1 1 0
2020-04-01 42 8 1 0
2020-03-30 34 1 1 0
2020-03-29 33 1 1 0
2020-03-28 32 2 1 0
2020-03-27 30 6 1 0
2020-03-26 24 5 1 0
2020-03-25 19 2 1 0
2020-03-24 17 1 1 0
2020-03-23 16 6 1 0
2020-03-22 10 2 1 0
2020-03-20 8 3 1 0
2020-03-19 5 1 1 1
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2020-03-17 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: 0:03:02.501074