Germany: SK Landshut (Bayern)¶

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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 11:51:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Landshut", weeks=5);
2023-03-07T11:51:13.996148 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 125 125 150 150 175 175 7-day incidence rate (per 100K people) 82.0 SK Landshut, 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 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1 2 3 4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Landshut cases daily growth factor Germany-SK Landshut cases daily growth factor (rolling mean) Germany-SK Landshut 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-SK Landshut deaths daily growth factor Germany-SK Landshut deaths daily growth factor (rolling mean) Germany-SK Landshut 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-SK Landshut doubling time cases (rolling mean) Germany-SK Landshut doubling time deaths (rolling mean) 0.00 14.63 29.26 43.89 daily change Germany-SK Landshut new cases (rolling 7d mean) Germany-SK Landshut new cases 0.000 0.731 1.463 2.194 2.926 daily change Germany-SK Landshut new deaths (rolling 7d mean) Germany-SK Landshut new deaths 0.0 65.4 130.8 196.1 261.5 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="SK Landshut");
2023-03-07T11:51:29.856134 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) 82.0 SK Landshut, 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 5 10 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-SK Landshut cases daily growth factor Germany-SK Landshut cases daily growth factor (rolling mean) Germany-SK Landshut 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-SK Landshut deaths daily growth factor Germany-SK Landshut deaths daily growth factor (rolling mean) Germany-SK Landshut 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 cases doubling time [days] Germany-SK Landshut doubling time cases (rolling mean) Germany-SK Landshut doubling time deaths (rolling mean) 0.0 146.3 292.6 438.9 daily change Germany-SK Landshut new cases (rolling 7d mean) Germany-SK Landshut new cases 0.00 3.66 7.31 daily change Germany-SK Landshut new deaths (rolling 7d mean) Germany-SK Landshut new deaths 0.0 55.8 111.7 167.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Landshut", dates="2020-03-15:");
2023-03-07T11:53:56.703667 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: SK Landshut SK Landshut 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 SK Landshut Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Landshut")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Landshut")
print(f'Population of country="Germany", subregion="SK Landshut": {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="SK Landshut": 73150 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 35844 10 229 0
2023-03-03 35834 6 229 0
2023-03-02 35828 8 229 0
2023-03-01 35820 20 229 0
2023-02-28 35800 16 229 0
2023-02-27 35784 37 229 0
2023-02-24 35747 20 229 3
2023-02-23 35727 21 226 0
2023-02-22 35706 46 226 0
2023-02-20 35660 50 226 0
2023-02-17 35610 10 226 1
2023-02-16 35600 22 225 0
2023-02-15 35578 32 225 1
2023-02-14 35546 25 224 1
2023-02-13 35521 39 223 2
2023-02-10 35482 13 221 0
2023-02-09 35469 17 221 0
2023-02-08 35452 17 221 1
2023-02-07 35435 26 220 1
2023-02-06 35409 39 219 0
2023-02-03 35370 10 219 0
2023-02-02 35360 23 219 0
2023-02-01 35337 19 219 0
2023-01-31 35318 32 219 0
2023-01-30 35286 25 219 0
2023-01-27 35261 8 219 0
2023-01-26 35253 21 219 0
2023-01-25 35232 10 219 0
2023-01-24 35222 13 219 1
2023-01-23 35209 17 218 0
2023-01-20 35192 6 218 0
2023-01-19 35186 3 218 0
2023-01-18 35183 6 218 0
2023-01-17 35177 21 218 1
2023-01-16 35156 24 217 1
2023-01-13 35132 3 216 0
2023-01-12 35129 11 216 0
2023-01-11 35118 13 216 0
2023-01-10 35105 23 216 0
2023-01-09 35082 28 216 0
2023-01-05 35054 14 216 0
2023-01-04 35040 29 216 1
2023-01-03 35011 23 215 0
2023-01-02 34988 7 215 0
2022-12-30 34981 10 215 0
2022-12-29 34971 13 215 0
2022-12-28 34958 18 215 1
2022-12-27 34940 44 214 2
2022-12-23 34896 6 212 0
2022-12-22 34890 12 212 0
2022-12-21 34878 23 212 1
2022-12-20 34855 6 211 1
2022-12-19 34849 28 210 0
2022-12-16 34821 17 210 1
2022-12-15 34804 6 209 0
2022-12-14 34798 12 209 0
2022-12-13 34786 19 209 1
2022-12-12 34767 22 208 0
2022-12-09 34745 12 208 1
2022-12-08 34733 6 207 0
2022-12-07 34727 14 207 0
2022-12-06 34713 6 207 0
2022-12-05 34707 23 207 0
2022-12-02 34684 2 207 0
2022-12-01 34682 12 207 0
2022-11-30 34670 10 207 0
2022-11-29 34660 21 207 0
2022-11-28 34639 19 207 0
2022-11-25 34620 13 207 0
2022-11-24 34607 28 207 0
2022-11-23 34579 3 207 0
2022-11-22 34576 7 207 0
2022-11-21 34569 18 207 1
2022-11-18 34551 11 206 0
2022-11-17 34540 15 206 0
2022-11-16 34525 10 206 0
2022-11-15 34515 16 206 0
2022-11-14 34499 15 206 0
2022-11-11 34484 20 206 0
2022-11-10 34464 20 206 0
2022-11-09 34444 16 206 0
2022-11-08 34428 35 206 0
2022-11-07 34393 57 206 0
2022-11-04 34336 5 206 0
2022-11-03 34331 33 206 0
2022-11-02 34298 48 206 0
2022-10-31 34250 58 206 1
2022-10-28 34192 15 205 0
2022-10-27 34177 31 205 2
2022-10-26 34146 42 203 0
2022-10-25 34104 65 203 0
2022-10-24 34039 62 203 0
2022-10-21 33977 34 203 0
2022-10-20 33943 55 203 0
2022-10-19 33888 102 203 1
2022-10-18 33786 113 202 0
2022-10-17 33673 151 202 1
2022-10-14 33522 59 201 0
2022-10-13 33463 71 201 0
2022-10-12 33392 147 201 0
2022-10-11 33245 193 201 1
2022-10-10 33052 200 200 4
2022-10-07 32852 75 196 0
2022-10-06 32777 88 196 1
2022-10-05 32689 152 195 2
2022-10-04 32537 212 193 1
2022-09-30 32325 28 192 0
2022-09-29 32297 82 192 0
2022-09-28 32215 118 192 0
2022-09-27 32097 119 192 0
2022-09-26 31978 105 192 0
2022-09-23 31873 20 192 0
2022-09-22 31853 69 192 0
2022-09-21 31784 45 192 0
2022-09-20 31739 25 192 0
2022-09-19 31714 77 192 0
2022-09-16 31637 31 192 0
2022-09-15 31606 24 192 0
2022-09-14 31582 32 192 0
2022-09-13 31550 26 192 0
2022-09-12 31524 64 192 0
2022-09-09 31460 16 192 0
2022-09-08 31444 24 192 0
2022-09-07 31420 26 192 0
2022-09-06 31394 50 192 0
2022-09-05 31344 44 192 0
2022-09-02 31300 21 192 0
2022-09-01 31279 28 192 0
2022-08-31 31251 47 192 0
2022-08-30 31204 34 192 0
2022-08-29 31170 59 192 1
2022-08-26 31111 35 191 0
2022-08-25 31076 30 191 0
2022-08-24 31046 9 191 0
2022-08-23 31037 42 191 0
2022-08-22 30995 67 191 0
2022-08-19 30928 9 191 0
2022-08-18 30919 64 191 0
2022-08-17 30855 30 191 0
2022-08-16 30825 42 191 2
2022-08-12 30783 10 189 0
2022-08-11 30773 26 189 0
2022-08-10 30747 24 189 0
2022-08-09 30723 54 189 0
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2022-08-05 30589 15 189 0
2022-08-04 30574 38 189 0
2022-08-03 30536 44 189 0
2022-08-02 30492 96 189 0
2022-08-01 30396 101 189 0
2022-07-29 30295 25 189 1
2022-07-28 30270 82 188 1
2022-07-27 30188 88 187 0
2022-07-26 30100 128 187 0
2022-07-25 29972 184 187 0
2022-07-22 29788 48 187 1
2022-07-21 29740 108 186 0
2022-07-20 29632 121 186 0
2022-07-19 29511 174 186 0
2022-07-18 29337 171 186 0
2022-07-15 29166 55 186 0
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2022-07-06 28361 165 183 0
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2022-07-01 27930 47 183 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: 0:03:16.978441