Germany: LK Waldeck-Frankenberg (Hessen)¶

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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 12:44:13 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Waldeck-Frankenberg", weeks=5);
2023-03-07T12:44:26.067397 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 75 75 100 100 125 125 7-day incidence rate (per 100K people) 70.9 LK Waldeck-Frankenberg, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 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 Waldeck-Frankenberg cases daily growth factor Germany-LK Waldeck-Frankenberg cases daily growth factor (rolling mean) Germany-LK Waldeck-Frankenberg 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 Waldeck-Frankenberg deaths daily growth factor Germany-LK Waldeck-Frankenberg deaths daily growth factor (rolling mean) Germany-LK Waldeck-Frankenberg estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-LK Waldeck-Frankenberg doubling time cases (rolling mean) 0.00 15.65 31.30 46.95 daily change Germany-LK Waldeck-Frankenberg new cases (rolling 7d mean) Germany-LK Waldeck-Frankenberg new cases 0.000 0.313 0.626 0.939 daily change Germany-LK Waldeck-Frankenberg new deaths (rolling 7d mean) Germany-LK Waldeck-Frankenberg new deaths 0.000 0.388 0.777
In [4]:
overview(country="Germany", subregion="LK Waldeck-Frankenberg");
2023-03-07T12:44:46.640277 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) 70.9 LK Waldeck-Frankenberg, 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 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 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 Waldeck-Frankenberg cases daily growth factor Germany-LK Waldeck-Frankenberg cases daily growth factor (rolling mean) Germany-LK Waldeck-Frankenberg 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 Waldeck-Frankenberg deaths daily growth factor Germany-LK Waldeck-Frankenberg deaths daily growth factor (rolling mean) Germany-LK Waldeck-Frankenberg estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Waldeck-Frankenberg doubling time cases (rolling mean) Germany-LK Waldeck-Frankenberg doubling time deaths (rolling mean) 0 313 626 daily change Germany-LK Waldeck-Frankenberg new cases (rolling 7d mean) Germany-LK Waldeck-Frankenberg new cases 0.00 3.13 6.26 daily change Germany-LK Waldeck-Frankenberg new deaths (rolling 7d mean) Germany-LK Waldeck-Frankenberg new deaths 0.0 31.9 63.8 95.6 127.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Waldeck-Frankenberg", dates="2020-03-15:");
2023-03-07T12:47:07.967172 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 Waldeck-Frankenberg LK Waldeck-Frankenberg 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 Waldeck-Frankenberg Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Waldeck-Frankenberg")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Waldeck-Frankenberg")
print(f'Population of country="Germany", subregion="LK Waldeck-Frankenberg": {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 Waldeck-Frankenberg": 156513 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 66839 11 223 0
2023-03-03 66828 17 223 0
2023-03-02 66811 12 223 0
2023-03-01 66799 41 223 0
2023-02-28 66758 30 223 0
2023-02-27 66728 57 223 0
2023-02-24 66671 38 223 0
2023-02-23 66633 41 223 0
2023-02-22 66592 28 223 0
2023-02-21 66564 21 223 0
2023-02-20 66543 43 223 0
2023-02-17 66500 20 223 0
2023-02-16 66480 18 223 0
2023-02-15 66462 28 223 0
2023-02-14 66434 41 223 0
2023-02-13 66393 35 223 0
2023-02-10 66358 11 223 0
2023-02-09 66347 28 223 1
2023-02-08 66319 26 222 0
2023-02-07 66293 28 222 0
2023-02-06 66265 27 222 0
2023-02-03 66238 19 222 0
2023-02-02 66219 8 222 0
2023-02-01 66211 22 222 0
2023-01-31 66189 9 222 0
2023-01-30 66180 11 222 0
2023-01-27 66169 8 222 0
2023-01-26 66161 27 222 0
2023-01-25 66134 22 222 0
2023-01-24 66112 23 222 0
2023-01-23 66089 17 222 0
2023-01-20 66072 8 222 0
2023-01-19 66064 10 222 0
2023-01-18 66054 17 222 0
2023-01-17 66037 19 222 0
2023-01-16 66018 14 222 0
2023-01-13 66004 62 222 0
2023-01-12 65942 18 222 0
2023-01-11 65924 27 222 0
2023-01-10 65897 38 222 0
2023-01-09 65859 31 222 0
2023-01-06 65828 22 222 0
2023-01-05 65806 31 222 1
2023-01-04 65775 49 221 0
2023-01-03 65726 66 221 0
2023-01-02 65660 226 221 1
2022-12-23 65434 57 220 0
2022-12-22 65377 75 220 0
2022-12-21 65302 67 220 0
2022-12-20 65235 56 220 0
2022-12-19 65179 91 220 0
2022-12-16 65088 45 220 0
2022-12-15 65043 25 220 0
2022-12-14 65018 66 220 0
2022-12-13 64952 47 220 0
2022-12-12 64905 94 220 1
2022-12-09 64811 42 219 0
2022-12-08 64769 51 219 0
2022-12-07 64718 51 219 0
2022-12-06 64667 81 219 0
2022-12-05 64586 104 219 0
2022-12-02 64482 27 219 0
2022-12-01 64455 48 219 0
2022-11-30 64407 122 219 0
2022-11-29 64285 45 219 0
2022-11-28 64240 56 219 0
2022-11-25 64184 35 219 0
2022-11-24 64149 40 219 0
2022-11-23 64109 83 219 0
2022-11-22 64026 48 219 0
2022-11-21 63978 60 219 0
2022-11-18 63918 24 219 0
2022-11-17 63894 43 219 0
2022-11-16 63851 33 219 0
2022-11-15 63818 64 219 0
2022-11-14 63754 65 219 0
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2022-11-09 63618 56 219 1
2022-11-08 63562 79 218 0
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2022-11-04 63398 41 218 0
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2022-11-02 63274 89 218 0
2022-11-01 63185 122 218 1
2022-10-31 63063 101 217 1
2022-10-28 62962 66 216 0
2022-10-27 62896 110 216 0
2022-10-26 62786 108 216 0
2022-10-25 62678 149 216 0
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2022-10-21 62322 127 216 0
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2022-10-19 61982 271 216 0
2022-10-18 61711 272 216 0
2022-10-17 61439 250 216 1
2022-10-14 61189 174 215 0
2022-10-13 61015 167 215 0
2022-10-12 60848 328 215 0
2022-10-11 60520 287 215 0
2022-10-10 60233 287 215 0
2022-10-08 59946 1 215 0
2022-10-07 59945 139 215 1
2022-10-06 59806 418 214 1
2022-10-05 59388 157 213 0
2022-10-04 59231 218 213 0
2022-09-30 59013 139 213 0
2022-09-29 58874 125 213 0
2022-09-28 58749 202 213 0
2022-09-27 58547 120 213 0
2022-09-26 58427 91 213 0
2022-09-23 58336 77 213 0
2022-09-22 58259 52 213 0
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2022-09-19 58048 84 213 1
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2022-08-31 57232 62 211 0
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2020-12-29 2730 81 76 5
2020-12-28 2649 14 71 0
2020-12-27 2635 19 71 1
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2020-12-21 2219 25 53 4
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2020-12-11 1662 63 37 1
2020-12-10 1599 53 36 2
2020-12-09 1546 45 34 1
2020-12-08 1501 28 33 1
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2020-12-02 1357 42 28 1
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2020-11-30 1291 14 26 0
2020-11-29 1277 6 26 0
2020-11-28 1271 25 26 1
2020-11-27 1246 13 25 0
2020-11-26 1233 40 25 0
2020-11-25 1193 34 25 0
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2020-11-20 1120 24 24 0
2020-11-19 1096 74 24 0
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2020-07-18 197 6 5 0
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2020-07-11 185 5 5 0
2020-07-10 180 4 5 0
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2020-06-23 174 1 5 0
2020-06-16 173 1 5 0
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2020-06-03 170 2 5 0
2020-05-27 168 2 5 0
2020-05-26 166 4 5 0
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2020-04-14 114 1 3 0
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2020-03-18 22 3 0 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: 0:03:18.253770