Germany: LK Neunkirchen (Saarland)¶

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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 15:11:11 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Neunkirchen", weeks=5);
2023-03-07T15:11:24.333841 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 200 200 300 300 7-day incidence rate (per 100K people) 214.0 LK Neunkirchen, 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 60 80 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Neunkirchen cases daily growth factor Germany-LK Neunkirchen cases daily growth factor (rolling mean) Germany-LK Neunkirchen estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Neunkirchen deaths daily growth factor Germany-LK Neunkirchen deaths daily growth factor (rolling mean) Germany-LK Neunkirchen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 500 1000 1500 2000 cases doubling time [days] Germany-LK Neunkirchen doubling time cases (rolling mean) 0.0 26.2 52.3 78.5 104.7 daily change Germany-LK Neunkirchen new cases (rolling 7d mean) Germany-LK Neunkirchen new cases 0.000 0.654 1.308 1.963 daily change Germany-LK Neunkirchen new deaths (rolling 7d mean) Germany-LK Neunkirchen new deaths 0.000 0.204 0.409 0.613 0.818
In [4]:
overview(country="Germany", subregion="LK Neunkirchen");
2023-03-07T15:11:43.083069 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 1000 1000 2000 2000 3000 3000 7-day incidence rate (per 100K people) 214.0 LK Neunkirchen, Germany, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 200 400 600 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 1 2 3 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 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 Neunkirchen cases daily growth factor Germany-LK Neunkirchen cases daily growth factor (rolling mean) Germany-LK Neunkirchen estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.0 0.0 0.5 0.5 1.0 1.0 R & growth factor (based on deaths) Germany-LK Neunkirchen deaths daily growth factor Germany-LK Neunkirchen deaths daily growth factor (rolling mean) Germany-LK Neunkirchen estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 2000 4000 6000 cases doubling time [days] Germany-LK Neunkirchen doubling time cases (rolling mean) Germany-LK Neunkirchen doubling time deaths (rolling mean) 0 262 523 785 daily change Germany-LK Neunkirchen new cases (rolling 7d mean) Germany-LK Neunkirchen new cases 0.000 1.308 2.617 3.925 daily change Germany-LK Neunkirchen new deaths (rolling 7d mean) Germany-LK Neunkirchen new deaths 0.0 49.1 98.1 147.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Neunkirchen", dates="2020-03-15:");
2023-03-07T15:14:21.804333 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 Neunkirchen LK Neunkirchen 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 Neunkirchen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Neunkirchen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Neunkirchen")
print(f'Population of country="Germany", subregion="LK Neunkirchen": {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 Neunkirchen": 130847 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 65599 38 247 0
2023-03-03 65561 18 247 0
2023-03-02 65543 37 247 0
2023-03-01 65506 73 247 0
2023-02-28 65433 114 247 0
2023-02-27 65319 125 247 0
2023-02-24 65194 51 247 0
2023-02-23 65143 74 247 0
2023-02-22 65069 124 247 0
2023-02-21 64945 106 247 0
2023-02-17 64839 37 247 0
2023-02-16 64802 43 247 0
2023-02-15 64759 48 247 0
2023-02-14 64711 79 247 1
2023-02-13 64632 42 246 0
2023-02-10 64590 27 246 0
2023-02-09 64563 29 246 0
2023-02-08 64534 34 246 0
2023-02-07 64500 76 246 0
2023-02-06 64424 58 246 2
2023-02-03 64366 21 244 0
2023-02-02 64345 43 244 0
2023-02-01 64302 38 244 0
2023-01-31 64264 34 244 0
2023-01-30 64230 40 244 0
2023-01-27 64190 19 244 0
2023-01-26 64171 15 244 0
2023-01-25 64156 29 244 0
2023-01-24 64127 32 244 0
2023-01-23 64095 30 244 1
2023-01-20 64065 11 243 0
2023-01-19 64054 19 243 0
2023-01-18 64035 19 243 0
2023-01-17 64016 33 243 1
2023-01-16 63983 34 242 0
2023-01-13 63949 6 242 0
2023-01-12 63943 24 242 0
2023-01-11 63919 30 242 0
2023-01-10 63889 49 242 0
2023-01-09 63840 42 242 0
2023-01-06 63798 21 242 0
2023-01-05 63777 44 242 0
2023-01-04 63733 56 242 0
2023-01-03 63677 88 242 1
2023-01-02 63589 132 241 2
2022-12-30 63457 53 239 0
2022-12-29 63404 53 239 0
2022-12-28 63351 59 239 0
2022-12-27 63292 64 239 0
2022-12-23 63228 54 239 0
2022-12-22 63174 69 239 1
2022-12-21 63105 82 238 0
2022-12-20 63023 72 238 0
2022-12-19 62951 95 238 1
2022-12-16 62856 42 237 0
2022-12-15 62814 48 237 0
2022-12-14 62766 48 237 0
2022-12-13 62718 79 237 0
2022-12-12 62639 70 237 1
2022-12-09 62569 35 236 0
2022-12-08 62534 43 236 0
2022-12-07 62491 58 236 0
2022-12-06 62433 80 236 0
2022-12-05 62353 60 236 0
2022-12-02 62293 37 236 0
2022-12-01 62256 41 236 1
2022-11-30 62215 61 235 0
2022-11-29 62154 83 235 1
2022-11-28 62071 97 234 1
2022-11-25 61974 66 233 0
2022-11-24 61908 44 233 0
2022-11-23 61864 73 233 0
2022-11-22 61791 78 233 0
2022-11-21 61713 72 233 1
2022-11-18 61641 56 232 1
2022-11-17 61585 47 231 0
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2022-11-15 61485 90 231 0
2022-11-14 61395 74 231 0
2022-11-11 61321 54 231 0
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2022-11-09 61226 57 231 1
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2022-11-04 61004 57 229 0
2022-11-03 60947 84 229 0
2022-11-02 60863 110 229 0
2022-10-31 60753 118 229 0
2022-10-28 60635 87 229 0
2022-10-27 60548 95 229 1
2022-10-26 60453 150 228 1
2022-10-25 60303 232 227 1
2022-10-24 60071 284 226 0
2022-10-21 59787 151 226 1
2022-10-20 59636 246 225 0
2022-10-19 59390 322 225 2
2022-10-18 59068 344 223 1
2022-10-17 58724 394 222 0
2022-10-14 58330 212 222 0
2022-10-13 58118 324 222 0
2022-10-12 57794 329 222 0
2022-10-11 57465 410 222 2
2022-10-10 57055 541 220 0
2022-10-07 56514 529 220 1
2022-10-06 55985 331 219 0
2022-10-05 55654 320 219 0
2022-10-04 55334 270 219 0
2022-09-30 55064 175 219 2
2022-09-29 54889 233 217 0
2022-09-28 54656 264 217 0
2022-09-27 54392 230 217 0
2022-09-26 54162 214 217 1
2022-09-23 53948 122 216 0
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2022-09-16 53248 61 216 0
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2022-09-05 52526 72 216 0
2022-09-02 52454 61 216 0
2022-09-01 52393 45 216 0
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2022-08-29 52183 104 216 0
2022-08-26 52079 53 216 0
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2022-08-24 51971 53 216 0
2022-08-23 51918 104 216 0
2022-08-22 51814 101 216 0
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2022-08-17 51558 152 216 1
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2022-08-05 50678 118 215 0
2022-08-04 50560 119 215 0
2022-08-03 50441 136 215 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: 0:03:59.361983