Germany: LK Merzig-Wadern (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 15:11:30 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Merzig-Wadern", weeks=5);
2023-03-07T15:11:43.286994 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 200 200 300 300 400 400 7-day incidence rate (per 100K people) 261.1 LK Merzig-Wadern, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 50 100 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 0.8 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Merzig-Wadern cases daily growth factor Germany-LK Merzig-Wadern cases daily growth factor (rolling mean) Germany-LK Merzig-Wadern 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 Merzig-Wadern deaths daily growth factor Germany-LK Merzig-Wadern deaths daily growth factor (rolling mean) Germany-LK Merzig-Wadern 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 Merzig-Wadern doubling time cases (rolling mean) 0.0 51.7 103.4 daily change Germany-LK Merzig-Wadern new cases (rolling 7d mean) Germany-LK Merzig-Wadern new cases 0.000 0.207 0.414 0.621 0.827 daily change Germany-LK Merzig-Wadern new deaths (rolling 7d mean) Germany-LK Merzig-Wadern new deaths 0.000 0.207 0.413 0.620 0.826
In [4]:
overview(country="Germany", subregion="LK Merzig-Wadern");
2023-03-07T15:11:59.827130 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) 261.1 LK Merzig-Wadern, 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 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Merzig-Wadern cases daily growth factor Germany-LK Merzig-Wadern cases daily growth factor (rolling mean) Germany-LK Merzig-Wadern 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 Merzig-Wadern deaths daily growth factor Germany-LK Merzig-Wadern deaths daily growth factor (rolling mean) Germany-LK Merzig-Wadern 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 cases doubling time [days] Germany-LK Merzig-Wadern doubling time cases (rolling mean) Germany-LK Merzig-Wadern doubling time deaths (rolling mean) 0.0 206.9 413.7 daily change Germany-LK Merzig-Wadern new cases (rolling 7d mean) Germany-LK Merzig-Wadern new cases 0.000 2.069 4.137 daily change Germany-LK Merzig-Wadern new deaths (rolling 7d mean) Germany-LK Merzig-Wadern new deaths 0.0 51.2 102.5 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Merzig-Wadern", dates="2020-03-15:");
2023-03-07T15:14:21.711193 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 Merzig-Wadern LK Merzig-Wadern 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 Merzig-Wadern Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Merzig-Wadern")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Merzig-Wadern")
print(f'Population of country="Germany", subregion="LK Merzig-Wadern": {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 Merzig-Wadern": 103426 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 54537 29 190 0
2023-03-03 54508 19 190 0
2023-03-02 54489 36 190 0
2023-03-01 54453 71 190 0
2023-02-28 54382 115 190 0
2023-02-27 54267 139 190 0
2023-02-24 54128 82 190 0
2023-02-23 54046 56 190 0
2023-02-22 53990 57 190 1
2023-02-21 53933 92 189 0
2023-02-17 53841 18 189 0
2023-02-16 53823 32 189 0
2023-02-15 53791 31 189 0
2023-02-14 53760 41 189 0
2023-02-13 53719 52 189 0
2023-02-10 53667 17 189 0
2023-02-09 53650 21 189 0
2023-02-08 53629 29 189 0
2023-02-07 53600 26 189 0
2023-02-06 53574 47 189 1
2023-02-03 53527 16 188 0
2023-02-02 53511 19 188 0
2023-02-01 53492 34 188 0
2023-01-31 53458 24 188 0
2023-01-30 53434 54 188 0
2023-01-27 53380 12 188 0
2023-01-26 53368 13 188 0
2023-01-25 53355 20 188 0
2023-01-24 53335 24 188 0
2023-01-23 53311 14 188 1
2023-01-20 53297 10 187 0
2023-01-19 53287 13 187 0
2023-01-18 53274 14 187 0
2023-01-17 53260 17 187 0
2023-01-16 53243 22 187 0
2023-01-13 53221 14 187 1
2023-01-12 53207 6 186 0
2023-01-11 53201 18 186 0
2023-01-10 53183 31 186 0
2023-01-09 53152 35 186 0
2023-01-06 53117 20 186 0
2023-01-05 53097 43 186 0
2023-01-04 53054 31 186 0
2023-01-03 53023 48 186 0
2023-01-02 52975 56 186 0
2022-12-30 52919 24 186 0
2022-12-29 52895 52 186 0
2022-12-28 52843 50 186 0
2022-12-27 52793 107 186 2
2022-12-23 52686 53 184 0
2022-12-22 52633 27 184 1
2022-12-21 52606 57 183 0
2022-12-20 52549 51 183 1
2022-12-19 52498 59 182 2
2022-12-16 52439 41 180 0
2022-12-15 52398 34 180 0
2022-12-14 52364 47 180 0
2022-12-13 52317 89 180 1
2022-12-12 52228 64 179 1
2022-12-09 52164 43 178 0
2022-12-08 52121 43 178 0
2022-12-07 52078 60 178 0
2022-12-06 52018 69 178 0
2022-12-05 51949 76 178 1
2022-12-02 51873 37 177 0
2022-12-01 51836 46 177 0
2022-11-30 51790 51 177 0
2022-11-29 51739 106 177 0
2022-11-28 51633 59 177 0
2022-11-25 51574 22 177 0
2022-11-24 51552 49 177 0
2022-11-23 51503 38 177 0
2022-11-22 51465 48 177 0
2022-11-21 51417 48 177 0
2022-11-18 51369 25 177 0
2022-11-17 51344 18 177 1
2022-11-16 51326 36 176 0
2022-11-15 51290 47 176 0
2022-11-14 51243 50 176 0
2022-11-11 51193 29 176 0
2022-11-10 51164 33 176 0
2022-11-09 51131 51 176 0
2022-11-08 51080 50 176 0
2022-11-07 51030 57 176 1
2022-11-06 50973 34 175 0
2022-11-04 50939 68 175 0
2022-11-03 50871 79 175 0
2022-11-02 50792 102 175 0
2022-10-31 50690 138 175 0
2022-10-28 50552 85 175 0
2022-10-27 50467 103 175 2
2022-10-26 50364 107 173 1
2022-10-25 50257 154 172 0
2022-10-24 50103 179 172 0
2022-10-23 49924 46 172 0
2022-10-21 49878 141 172 0
2022-10-20 49737 182 172 1
2022-10-19 49555 243 171 2
2022-10-18 49312 253 169 0
2022-10-17 49059 368 169 1
2022-10-16 48691 49 168 0
2022-10-14 48642 244 168 3
2022-10-13 48398 255 165 2
2022-10-12 48143 324 163 1
2022-10-11 47819 413 162 0
2022-10-10 47406 562 162 1
2022-10-09 46844 94 161 0
2022-10-07 46750 378 161 0
2022-10-06 46372 441 161 0
2022-10-05 45931 530 161 0
2022-10-04 45401 387 161 2
2022-10-02 45014 58 159 1
2022-09-30 44956 177 158 0
2022-09-29 44779 162 158 0
2022-09-28 44617 208 158 1
2022-09-27 44409 237 157 0
2022-09-26 44172 92 157 0
2022-09-25 44080 69 157 0
2022-09-23 44011 88 157 0
2022-09-22 43923 98 157 0
2022-09-21 43825 71 157 0
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2022-09-07 43204 43 157 0
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2022-09-05 43107 37 157 0
2022-09-04 43070 32 157 0
2022-09-02 43038 24 157 0
2022-09-01 43014 31 157 0
2022-08-31 42983 62 157 0
2022-08-30 42921 56 157 0
2022-08-29 42865 28 157 0
2022-08-28 42837 33 157 0
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2022-08-25 42768 40 157 0
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2022-08-19 42534 34 157 0
2022-08-18 42500 119 157 0
2022-08-17 42381 45 157 0
2022-08-16 42336 46 157 0
2022-08-14 42290 46 157 0
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2022-08-09 42075 75 157 1
2022-08-08 42000 67 156 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: 0:03:39.575176