Germany: LK Altenkirchen (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 14:32:24 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Altenkirchen", weeks=5);
2023-03-07T14:32:46.571629 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 100 100 150 150 200 200 250 250 7-day incidence rate (per 100K people) 132.3 LK Altenkirchen, 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 0.2 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Altenkirchen cases daily growth factor Germany-LK Altenkirchen cases daily growth factor (rolling mean) Germany-LK Altenkirchen 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 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Altenkirchen deaths daily growth factor Germany-LK Altenkirchen deaths daily growth factor (rolling mean) Germany-LK Altenkirchen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Altenkirchen doubling time cases (rolling mean) 0.0 25.9 51.7 77.6 103.4 daily change Germany-LK Altenkirchen new cases (rolling 7d mean) Germany-LK Altenkirchen new cases 0.000 0.259 0.517 0.776 daily change Germany-LK Altenkirchen new deaths (rolling 7d mean) Germany-LK Altenkirchen new deaths 0.000 0.212 0.423 0.635 0.846
In [4]:
overview(country="Germany", subregion="LK Altenkirchen");
2023-03-07T14:33:05.695950 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 1500 1500 7-day incidence rate (per 100K people) 132.3 LK Altenkirchen, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Altenkirchen cases daily growth factor Germany-LK Altenkirchen cases daily growth factor (rolling mean) Germany-LK Altenkirchen estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 R & growth factor (based on deaths) Germany-LK Altenkirchen deaths daily growth factor Germany-LK Altenkirchen deaths daily growth factor (rolling mean) Germany-LK Altenkirchen 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 Altenkirchen doubling time cases (rolling mean) Germany-LK Altenkirchen doubling time deaths (rolling mean) 0.0 129.3 258.5 387.8 517.0 daily change Germany-LK Altenkirchen new cases (rolling 7d mean) Germany-LK Altenkirchen new cases 0.000 1.293 2.585 3.878 daily change Germany-LK Altenkirchen new deaths (rolling 7d mean) Germany-LK Altenkirchen new deaths 0 28 56 84 112 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Altenkirchen", dates="2020-03-15:");
2023-03-07T14:35:11.731805 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 Altenkirchen LK Altenkirchen 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 Altenkirchen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Altenkirchen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Altenkirchen")
print(f'Population of country="Germany", subregion="LK Altenkirchen": {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 Altenkirchen": 129261 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 51893 21 181 0
2023-03-03 51872 13 181 0
2023-03-02 51859 25 181 0
2023-03-01 51834 65 181 0
2023-02-28 51769 47 181 0
2023-02-27 51722 114 181 0
2023-02-24 51608 58 181 0
2023-02-23 51550 55 181 0
2023-02-22 51495 33 181 0
2023-02-21 51462 35 181 0
2023-02-20 51427 39 181 0
2023-02-19 51388 1 181 0
2023-02-17 51387 12 181 0
2023-02-16 51375 42 181 0
2023-02-15 51333 22 181 0
2023-02-14 51311 35 181 0
2023-02-13 51276 45 181 1
2023-02-10 51231 5 180 0
2023-02-09 51226 16 180 0
2023-02-08 51210 19 180 1
2023-02-07 51191 26 179 0
2023-02-06 51165 25 179 0
2023-02-03 51140 12 179 0
2023-02-02 51128 16 179 0
2023-02-01 51112 7 179 0
2023-01-31 51105 19 179 1
2023-01-30 51086 22 178 0
2023-01-27 51064 6 178 0
2023-01-26 51058 6 178 0
2023-01-25 51052 17 178 0
2023-01-24 51035 8 178 0
2023-01-23 51027 17 178 0
2023-01-20 51010 8 178 0
2023-01-19 51002 11 178 0
2023-01-18 50991 8 178 0
2023-01-17 50983 21 178 0
2023-01-16 50962 21 178 0
2023-01-13 50941 15 178 0
2023-01-12 50926 16 178 0
2023-01-11 50910 21 178 0
2023-01-10 50889 21 178 0
2023-01-09 50868 38 178 0
2023-01-06 50830 12 178 0
2023-01-05 50818 30 178 0
2023-01-04 50788 56 178 0
2023-01-03 50732 46 178 0
2023-01-02 50686 76 178 0
2022-12-30 50610 32 178 0
2022-12-29 50578 50 178 0
2022-12-28 50528 53 178 1
2022-12-27 50475 103 177 0
2022-12-23 50372 48 177 1
2022-12-22 50324 67 176 1
2022-12-21 50257 82 175 0
2022-12-20 50175 76 175 2
2022-12-19 50099 92 173 0
2022-12-16 50007 41 173 1
2022-12-15 49966 49 172 0
2022-12-14 49917 61 172 0
2022-12-13 49856 55 172 1
2022-12-12 49801 71 171 2
2022-12-09 49730 30 169 0
2022-12-08 49700 47 169 0
2022-12-07 49653 34 169 0
2022-12-06 49619 44 169 0
2022-12-05 49575 55 169 0
2022-12-02 49520 24 169 0
2022-12-01 49496 34 169 0
2022-11-30 49462 30 169 0
2022-11-29 49432 33 169 0
2022-11-28 49399 92 169 0
2022-11-24 49307 29 169 1
2022-11-23 49278 64 168 0
2022-11-22 49214 52 168 0
2022-11-21 49162 99 168 1
2022-11-18 49063 44 167 0
2022-11-17 49019 45 167 0
2022-11-16 48974 62 167 0
2022-11-15 48912 93 167 2
2022-11-14 48819 89 165 0
2022-11-11 48730 41 165 0
2022-11-10 48689 26 165 0
2022-11-09 48663 69 165 0
2022-11-08 48594 69 165 0
2022-11-07 48525 97 165 0
2022-11-04 48428 64 165 0
2022-11-03 48364 87 165 0
2022-11-02 48277 103 165 0
2022-10-31 48174 176 165 2
2022-10-28 47998 84 163 0
2022-10-27 47914 109 163 0
2022-10-26 47805 109 163 0
2022-10-25 47696 117 163 0
2022-10-24 47579 235 163 0
2022-10-21 47344 122 163 0
2022-10-20 47222 142 163 0
2022-10-19 47080 220 163 0
2022-10-18 46860 209 163 0
2022-10-17 46651 285 163 1
2022-10-14 46366 153 162 0
2022-10-13 46213 188 162 1
2022-10-12 46025 227 161 2
2022-10-11 45798 214 159 1
2022-10-10 45584 440 158 3
2022-10-07 45144 222 155 0
2022-10-06 44922 101 155 0
2022-10-05 44821 245 155 0
2022-10-04 44576 228 155 0
2022-09-30 44348 112 155 0
2022-09-29 44236 99 155 0
2022-09-28 44137 126 155 0
2022-09-27 44011 115 155 0
2022-09-26 43896 170 155 1
2022-09-23 43726 77 154 0
2022-09-22 43649 106 154 0
2022-09-21 43543 94 154 0
2022-09-20 43449 51 154 0
2022-09-19 43398 119 154 0
2022-09-16 43279 30 154 0
2022-09-15 43249 72 154 0
2022-09-14 43177 71 154 0
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2022-09-08 42909 35 154 0
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2022-09-06 42802 43 154 0
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2022-09-02 42673 48 154 0
2022-09-01 42625 42 154 0
2022-08-31 42583 43 154 0
2022-08-30 42540 48 154 0
2022-08-29 42492 110 154 0
2022-08-26 42382 37 154 0
2022-08-25 42345 36 154 0
2022-08-24 42309 77 154 1
2022-08-23 42232 53 153 0
2022-08-22 42179 97 153 0
2022-08-19 42082 62 153 0
2022-08-18 42020 73 153 0
2022-08-17 41947 95 153 0
2022-08-16 41852 67 153 0
2022-08-15 41785 106 153 0
2022-08-12 41679 60 153 0
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2022-08-09 41502 70 153 0
2022-08-08 41432 156 153 0
2022-08-05 41276 62 153 1
2022-08-04 41214 77 152 0
2022-08-03 41137 78 152 0
2022-08-02 41059 93 152 0
2022-08-01 40966 151 152 0
2022-07-29 40815 70 152 1
2022-07-28 40745 112 151 0
2022-07-27 40633 134 151 0
2022-07-26 40499 139 151 0
2022-07-25 40360 255 151 1
2022-07-22 40105 75 150 0
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2022-05-30 33842 78 140 0
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2020-11-20 999 54 16 2
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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: 0:02:57.738047