Germany: LK Ostprignitz-Ruppin (Brandenburg)¶

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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 12:20:54 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Ostprignitz-Ruppin", weeks=5);
2023-03-07T12:21:09.992095 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 7-day incidence rate (per 100K people) 45.5 LK Ostprignitz-Ruppin, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 15 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Ostprignitz-Ruppin new deaths (rolling 7d mean) Germany-LK Ostprignitz-Ruppin new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on cases) Germany-LK Ostprignitz-Ruppin cases daily growth factor Germany-LK Ostprignitz-Ruppin cases daily growth factor (rolling mean) Germany-LK Ostprignitz-Ruppin estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Ostprignitz-Ruppin deaths daily growth factor Germany-LK Ostprignitz-Ruppin deaths daily growth factor (rolling mean) Germany-LK Ostprignitz-Ruppin estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Ostprignitz-Ruppin doubling time cases (rolling mean) 0.00 4.94 9.88 14.82 19.77 daily change Germany-LK Ostprignitz-Ruppin new cases (rolling 7d mean) Germany-LK Ostprignitz-Ruppin new cases 0.000 0.220 0.439 0.659 0.878
In [4]:
overview(country="Germany", subregion="LK Ostprignitz-Ruppin");
2023-03-07T12:21:28.109172 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) 45.5 LK Ostprignitz-Ruppin, 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Ostprignitz-Ruppin cases daily growth factor Germany-LK Ostprignitz-Ruppin cases daily growth factor (rolling mean) Germany-LK Ostprignitz-Ruppin estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Ostprignitz-Ruppin deaths daily growth factor Germany-LK Ostprignitz-Ruppin deaths daily growth factor (rolling mean) Germany-LK Ostprignitz-Ruppin 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 8000 cases doubling time [days] Germany-LK Ostprignitz-Ruppin doubling time cases (rolling mean) Germany-LK Ostprignitz-Ruppin doubling time deaths (rolling mean) 0.0 197.7 395.3 593.0 daily change Germany-LK Ostprignitz-Ruppin new cases (rolling 7d mean) Germany-LK Ostprignitz-Ruppin new cases 0.000 1.977 3.953 5.930 daily change Germany-LK Ostprignitz-Ruppin new deaths (rolling 7d mean) Germany-LK Ostprignitz-Ruppin new deaths 0.0 42.9 85.7 128.6 171.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Ostprignitz-Ruppin", dates="2020-03-15:");
2023-03-07T12:23:49.146796 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 Ostprignitz-Ruppin LK Ostprignitz-Ruppin 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 Ostprignitz-Ruppin Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Ostprignitz-Ruppin")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Ostprignitz-Ruppin")
print(f'Population of country="Germany", subregion="LK Ostprignitz-Ruppin": {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 Ostprignitz-Ruppin": 98829 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 40709 6 247 0
2023-03-03 40703 4 247 0
2023-03-02 40699 5 247 0
2023-03-01 40694 10 247 0
2023-02-28 40684 20 247 0
2023-02-27 40664 12 247 0
2023-02-24 40652 13 247 0
2023-02-23 40639 8 247 0
2023-02-22 40631 10 247 0
2023-02-21 40621 19 247 0
2023-02-20 40602 13 247 0
2023-02-17 40589 6 247 0
2023-02-16 40583 9 247 0
2023-02-15 40574 9 247 0
2023-02-14 40565 7 247 0
2023-02-13 40558 9 247 0
2023-02-10 40549 8 247 0
2023-02-08 40541 9 247 0
2023-02-07 40532 17 247 0
2023-02-06 40515 15 247 0
2023-02-03 40500 8 247 0
2023-02-02 40492 7 247 0
2023-02-01 40485 3 247 0
2023-01-31 40482 9 247 0
2023-01-30 40473 7 247 0
2023-01-27 40466 7 247 0
2023-01-26 40459 8 247 0
2023-01-25 40451 21 247 0
2023-01-24 40430 16 247 0
2023-01-23 40414 25 247 0
2023-01-21 40389 1 247 0
2023-01-20 40388 8 247 0
2023-01-19 40380 6 247 0
2023-01-18 40374 12 247 0
2023-01-17 40362 20 247 0
2023-01-16 40342 10 247 0
2023-01-14 40332 1 247 0
2023-01-13 40331 14 247 1
2023-01-12 40317 6 246 0
2023-01-11 40311 15 246 0
2023-01-10 40296 27 246 0
2023-01-09 40269 15 246 0
2023-01-06 40254 15 246 0
2023-01-05 40239 21 246 1
2023-01-04 40218 27 245 0
2023-01-03 40191 41 245 0
2023-01-02 40150 33 245 0
2023-01-01 40117 2 245 0
2022-12-31 40115 5 245 0
2022-12-30 40110 18 245 0
2022-12-29 40092 28 245 0
2022-12-28 40064 57 245 0
2022-12-27 40007 54 245 1
2022-12-24 39953 2 244 0
2022-12-23 39951 31 244 0
2022-12-22 39920 37 244 0
2022-12-21 39883 56 244 0
2022-12-20 39827 90 244 0
2022-12-19 39737 91 244 1
2022-12-16 39646 55 243 0
2022-12-15 39591 64 243 0
2022-12-14 39527 62 243 0
2022-12-13 39465 127 243 0
2022-12-12 39338 68 243 0
2022-12-09 39270 57 243 0
2022-12-08 39213 51 243 0
2022-12-07 39162 108 243 0
2022-12-06 39054 102 243 0
2022-12-05 38952 58 243 0
2022-12-03 38894 1 243 0
2022-12-02 38893 56 243 2
2022-12-01 38837 63 241 0
2022-11-30 38774 71 241 0
2022-11-29 38703 120 241 0
2022-11-28 38583 29 241 0
2022-11-27 38554 16 241 0
2022-11-25 38538 46 241 1
2022-11-24 38492 50 240 0
2022-11-23 38442 55 240 0
2022-11-22 38387 80 240 0
2022-11-21 38307 46 240 2
2022-11-19 38261 3 238 0
2022-11-18 38258 19 238 0
2022-11-17 38239 46 238 0
2022-11-16 38193 46 238 0
2022-11-15 38147 52 238 0
2022-11-14 38095 54 238 0
2022-11-11 38041 50 238 1
2022-11-10 37991 44 237 0
2022-11-09 37947 40 237 0
2022-11-08 37907 98 237 1
2022-11-07 37809 44 236 1
2022-11-04 37765 37 235 0
2022-11-03 37728 51 235 0
2022-11-02 37677 81 235 0
2022-11-01 37596 54 235 1
2022-10-29 37542 1 234 0
2022-10-28 37541 39 234 0
2022-10-27 37502 38 234 0
2022-10-26 37464 56 234 0
2022-10-25 37408 102 234 0
2022-10-24 37306 116 234 2
2022-10-21 37190 51 232 0
2022-10-20 37139 82 232 0
2022-10-19 37057 104 232 0
2022-10-18 36953 157 232 1
2022-10-17 36796 126 231 1
2022-10-16 36670 1 230 1
2022-10-15 36669 1 229 0
2022-10-14 36668 82 229 0
2022-10-13 36586 117 229 1
2022-10-12 36469 86 228 0
2022-10-11 36383 208 228 1
2022-10-10 36175 122 227 0
2022-10-09 36053 2 227 0
2022-10-08 36051 3 227 0
2022-10-07 36048 95 227 0
2022-10-06 35953 140 227 0
2022-10-05 35813 238 227 2
2022-10-04 35575 97 225 1
2022-09-30 35478 84 224 0
2022-09-29 35394 60 224 0
2022-09-28 35334 82 224 1
2022-09-27 35252 118 223 0
2022-09-26 35134 76 223 0
2022-09-23 35058 48 223 0
2022-09-22 35010 82 223 0
2022-09-21 34928 69 223 0
2022-09-20 34859 104 223 0
2022-09-19 34755 61 223 0
2022-09-16 34694 57 223 0
2022-09-15 34637 57 223 0
2022-09-14 34580 62 223 0
2022-09-13 34518 128 223 0
2022-09-12 34390 71 223 0
2022-09-09 34319 40 223 1
2022-09-08 34279 69 222 0
2022-09-07 34210 63 222 0
2022-09-06 34147 83 222 0
2022-09-05 34064 38 222 0
2022-09-02 34026 35 222 0
2022-09-01 33991 54 222 0
2022-08-31 33937 44 222 0
2022-08-30 33893 69 222 1
2022-08-29 33824 50 221 1
2022-08-26 33774 26 220 0
2022-08-25 33748 44 220 0
2022-08-24 33704 37 220 1
2022-08-23 33667 80 219 0
2022-08-22 33587 58 219 0
2022-08-19 33529 19 219 0
2022-08-18 33510 35 219 0
2022-08-17 33475 58 219 0
2022-08-16 33417 101 219 1
2022-08-15 33316 55 218 0
2022-08-12 33261 59 218 0
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2022-08-08 32971 42 217 0
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2022-08-02 32765 97 217 0
2022-08-01 32668 67 217 0
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2021-01-26 2729 68 111 3
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2021-01-15 2205 34 81 4
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2021-01-02 1515 6 42 1
2021-01-01 1509 7 41 0
2020-12-31 1502 26 41 4
2020-12-30 1476 75 37 2
2020-12-29 1401 74 35 5
2020-12-28 1327 82 30 5
2020-12-27 1245 13 25 4
2020-12-26 1232 12 21 1
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2020-12-22 1123 58 13 2
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2020-10-23 170 7 0 0
2020-10-22 163 12 0 0
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2020-05-02 68 1 0 0
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2020-03-30 14 2 0 0
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2020-03-19 6 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: 0:03:08.481122