Germany: LK Mecklenburgische Seenplatte (Mecklenburg-Vorpommern)¶

  • Homepage of project: https://oscovida.github.io
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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 12:58:57 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Mecklenburgische Seenplatte", weeks=5);
2023-03-07T12:59:08.094065 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 7-day incidence rate (per 100K people) 60.2 LK Mecklenburgische Seenplatte, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.2 0.4 0.6 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 Mecklenburgische Seenplatte cases daily growth factor Germany-LK Mecklenburgische Seenplatte cases daily growth factor (rolling mean) Germany-LK Mecklenburgische Seenplatte 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 Mecklenburgische Seenplatte deaths daily growth factor Germany-LK Mecklenburgische Seenplatte deaths daily growth factor (rolling mean) Germany-LK Mecklenburgische Seenplatte 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 Mecklenburgische Seenplatte doubling time cases (rolling mean) 0.00 12.88 25.75 38.63 daily change Germany-LK Mecklenburgische Seenplatte new cases (rolling 7d mean) Germany-LK Mecklenburgische Seenplatte new cases 0.000 0.515 1.030 1.545 daily change Germany-LK Mecklenburgische Seenplatte new deaths (rolling 7d mean) Germany-LK Mecklenburgische Seenplatte new deaths 0.000 0.324 0.649 0.973
In [4]:
overview(country="Germany", subregion="LK Mecklenburgische Seenplatte");
2023-03-07T12:59:26.040152 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 7-day incidence rate (per 100K people) 60.2 LK Mecklenburgische Seenplatte, 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 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1 2 3 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 Mecklenburgische Seenplatte cases daily growth factor Germany-LK Mecklenburgische Seenplatte cases daily growth factor (rolling mean) Germany-LK Mecklenburgische Seenplatte 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 Mecklenburgische Seenplatte deaths daily growth factor Germany-LK Mecklenburgische Seenplatte deaths daily growth factor (rolling mean) Germany-LK Mecklenburgische Seenplatte estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 cases doubling time [days] Germany-LK Mecklenburgische Seenplatte doubling time cases (rolling mean) Germany-LK Mecklenburgische Seenplatte doubling time deaths (rolling mean) 0.0 257.5 515.1 daily change Germany-LK Mecklenburgische Seenplatte new cases (rolling 7d mean) Germany-LK Mecklenburgische Seenplatte new cases 0.00 2.58 5.15 7.73 daily change Germany-LK Mecklenburgische Seenplatte new deaths (rolling 7d mean) Germany-LK Mecklenburgische Seenplatte new deaths 0.0 121.1 242.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Mecklenburgische Seenplatte", dates="2020-03-15:");
2023-03-07T13:02:03.722612 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 Mecklenburgische Seenplatte LK Mecklenburgische Seenplatte 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 Mecklenburgische Seenplatte Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Mecklenburgische Seenplatte")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Mecklenburgische Seenplatte")
print(f'Population of country="Germany", subregion="LK Mecklenburgische Seenplatte": {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 Mecklenburgische Seenplatte": 257525 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 94096 39 325 0
2023-03-03 94057 21 325 0
2023-03-02 94036 13 325 1
2023-03-01 94023 36 324 0
2023-02-28 93987 46 324 0
2023-02-27 93941 7 324 0
2023-02-26 93934 14 324 0
2023-02-25 93920 38 324 0
2023-02-24 93882 27 324 0
2023-02-23 93855 21 324 0
2023-02-22 93834 28 324 0
2023-02-21 93806 35 324 0
2023-02-20 93771 8 324 0
2023-02-19 93763 13 324 1
2023-02-18 93750 9 323 0
2023-02-17 93741 26 323 0
2023-02-16 93715 23 323 0
2023-02-15 93692 30 323 0
2023-02-14 93662 23 323 0
2023-02-13 93639 3 323 0
2023-02-12 93636 9 323 0
2023-02-11 93627 14 323 0
2023-02-10 93613 14 323 0
2023-02-09 93599 25 323 0
2023-02-08 93574 34 323 0
2023-02-07 93540 40 323 0
2023-02-06 93500 13 323 2
2023-02-05 93487 9 321 0
2023-02-04 93478 16 321 0
2023-02-03 93462 24 321 0
2023-02-02 93438 35 321 1
2023-02-01 93403 38 320 0
2023-01-31 93365 35 320 0
2023-01-30 93330 19 320 0
2023-01-29 93311 7 320 0
2023-01-28 93304 22 320 0
2023-01-27 93282 26 320 1
2023-01-26 93256 28 319 0
2023-01-25 93228 38 319 0
2023-01-24 93190 40 319 1
2023-01-23 93150 20 318 0
2023-01-21 93130 16 318 0
2023-01-20 93114 14 318 0
2023-01-19 93100 36 318 0
2023-01-18 93064 37 318 0
2023-01-17 93027 51 318 0
2023-01-16 92976 11 318 0
2023-01-15 92965 4 318 0
2023-01-14 92961 34 318 2
2023-01-13 92927 25 316 0
2023-01-12 92902 34 316 0
2023-01-11 92868 86 316 0
2023-01-10 92782 84 316 0
2023-01-09 92698 13 316 1
2023-01-08 92685 5 315 0
2023-01-07 92680 74 315 0
2023-01-06 92606 62 315 1
2023-01-05 92544 61 314 0
2023-01-04 92483 80 314 0
2023-01-03 92403 122 314 0
2023-01-02 92281 20 314 1
2023-01-01 92261 5 313 0
2022-12-31 92256 55 313 0
2022-12-30 92201 81 313 0
2022-12-29 92120 136 313 3
2022-12-28 91984 152 310 2
2022-12-27 91832 27 308 0
2022-12-26 91805 24 308 0
2022-12-25 91781 30 308 1
2022-12-24 91751 103 307 0
2022-12-23 91648 109 307 3
2022-12-22 91539 146 304 0
2022-12-21 91393 159 304 0
2022-12-20 91234 207 304 1
2022-12-19 91027 45 303 1
2022-12-18 90982 41 302 0
2022-12-17 90941 119 302 1
2022-12-16 90822 128 301 2
2022-12-15 90694 225 299 0
2022-12-14 90469 125 299 0
2022-12-13 90344 227 299 0
2022-12-12 90117 36 299 1
2022-12-11 90081 48 298 0
2022-12-10 90033 115 298 1
2022-12-09 89918 107 297 0
2022-12-08 89811 101 297 1
2022-12-07 89710 140 296 0
2022-12-06 89570 228 296 0
2022-12-05 89342 28 296 0
2022-12-04 89314 35 296 1
2022-12-03 89279 102 295 2
2022-12-02 89177 146 293 0
2022-12-01 89031 124 293 0
2022-11-30 88907 117 293 0
2022-11-29 88790 202 293 0
2022-11-28 88588 26 293 1
2022-11-27 88562 20 292 0
2022-11-26 88542 61 292 0
2022-11-25 88481 121 292 0
2022-11-24 88360 85 292 1
2022-11-23 88275 115 291 0
2022-11-22 88160 173 291 0
2022-11-21 87987 21 291 0
2022-11-20 87966 24 291 0
2022-11-19 87942 52 291 0
2022-11-18 87890 110 291 0
2022-11-17 87780 73 291 0
2022-11-16 87707 114 291 0
2022-11-15 87593 164 291 0
2022-11-14 87429 19 291 0
2022-11-13 87410 17 291 0
2022-11-12 87393 82 291 1
2022-11-11 87311 98 290 0
2022-11-10 87213 106 290 0
2022-11-09 87107 143 290 0
2022-11-08 86964 263 290 0
2022-11-07 86701 10 290 0
2022-11-06 86691 2 290 0
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2022-11-04 86538 187 290 1
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2022-11-02 86142 340 288 0
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2022-10-30 85739 39 288 1
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2022-10-28 85571 213 286 1
2022-10-27 85358 195 285 0
2022-10-26 85163 286 285 0
2022-10-25 84877 344 285 1
2022-10-24 84533 36 284 0
2022-10-23 84497 61 284 0
2022-10-22 84436 202 284 1
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2022-10-16 82791 61 281 0
2022-10-15 82730 240 281 0
2022-10-14 82490 258 281 1
2022-10-13 82232 312 280 1
2022-10-12 81920 353 279 0
2022-10-11 81567 397 279 1
2022-10-10 81170 56 278 1
2022-10-09 81114 64 277 1
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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: 0:03:55.757852