Germany: LK Tuttlingen (Baden-Württemberg)¶

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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 10:10:38 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Tuttlingen", weeks=5);
2023-03-07T10:10:49.711821 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 140 140 7-day incidence rate (per 100K people) 89.9 LK Tuttlingen, 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 1.4 1.4 R & growth factor (based on cases) Germany-LK Tuttlingen cases daily growth factor Germany-LK Tuttlingen cases daily growth factor (rolling mean) Germany-LK Tuttlingen 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 Tuttlingen deaths daily growth factor Germany-LK Tuttlingen deaths daily growth factor (rolling mean) Germany-LK Tuttlingen 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 Tuttlingen doubling time cases (rolling mean) 0.00 14.24 28.48 42.72 daily change Germany-LK Tuttlingen new cases (rolling 7d mean) Germany-LK Tuttlingen new cases 0.000 0.285 0.570 0.854 daily change Germany-LK Tuttlingen new deaths (rolling 7d mean) Germany-LK Tuttlingen new deaths 0.000 0.225 0.449 0.674 0.898
In [4]:
overview(country="Germany", subregion="LK Tuttlingen");
2023-03-07T10:11:08.580655 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) 89.9 LK Tuttlingen, 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 6 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 Tuttlingen cases daily growth factor Germany-LK Tuttlingen cases daily growth factor (rolling mean) Germany-LK Tuttlingen 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 R & growth factor (based on deaths) Germany-LK Tuttlingen deaths daily growth factor Germany-LK Tuttlingen deaths daily growth factor (rolling mean) Germany-LK Tuttlingen estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 5000 10000 cases doubling time [days] Germany-LK Tuttlingen doubling time cases (rolling mean) Germany-LK Tuttlingen doubling time deaths (rolling mean) 0.0 284.8 569.7 daily change Germany-LK Tuttlingen new cases (rolling 7d mean) Germany-LK Tuttlingen new cases 0.00 2.85 5.70 8.54 daily change Germany-LK Tuttlingen new deaths (rolling 7d mean) Germany-LK Tuttlingen new deaths 0.0 105.2 210.3 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Tuttlingen", dates="2020-03-15:");
2023-03-07T10:13:10.997247 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 Tuttlingen LK Tuttlingen 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 Tuttlingen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Tuttlingen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Tuttlingen")
print(f'Population of country="Germany", subregion="LK Tuttlingen": {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 Tuttlingen": 142414 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 66422 12 293 0
2023-03-03 66410 7 293 0
2023-03-02 66403 20 293 0
2023-03-01 66383 35 293 0
2023-02-28 66348 54 293 0
2023-02-27 66294 53 293 0
2023-02-24 66241 29 293 0
2023-02-23 66212 33 293 0
2023-02-22 66179 50 293 0
2023-02-21 66129 35 293 0
2023-02-20 66094 44 293 0
2023-02-17 66050 22 293 0
2023-02-16 66028 36 293 0
2023-02-15 65992 30 293 0
2023-02-14 65962 38 293 0
2023-02-13 65924 37 293 0
2023-02-10 65887 5 293 0
2023-02-09 65882 26 293 0
2023-02-08 65856 11 293 0
2023-02-07 65845 47 293 0
2023-02-06 65798 17 293 0
2023-02-03 65781 19 293 0
2023-02-02 65762 20 293 1
2023-02-01 65742 19 292 0
2023-01-31 65723 21 292 0
2023-01-30 65702 18 292 0
2023-01-27 65684 14 292 0
2023-01-26 65670 29 292 0
2023-01-25 65641 15 292 0
2023-01-24 65626 15 292 0
2023-01-23 65611 12 292 0
2023-01-20 65599 5 292 0
2023-01-19 65594 13 292 0
2023-01-18 65581 10 292 0
2023-01-17 65571 14 292 0
2023-01-16 65557 17 292 0
2023-01-13 65540 7 292 1
2023-01-12 65533 14 291 0
2023-01-11 65519 14 291 0
2023-01-10 65505 33 291 0
2023-01-09 65472 22 291 0
2023-01-05 65450 17 291 0
2023-01-04 65433 23 291 1
2023-01-03 65410 31 290 0
2023-01-02 65379 146 290 2
2022-12-23 65233 30 288 0
2022-12-22 65203 56 288 0
2022-12-21 65147 23 288 0
2022-12-20 65124 53 288 0
2022-12-19 65071 32 288 1
2022-12-16 65039 25 287 0
2022-12-15 65014 30 287 0
2022-12-14 64984 35 287 0
2022-12-13 64949 29 287 0
2022-12-12 64920 23 287 0
2022-12-09 64897 26 287 0
2022-12-08 64871 20 287 0
2022-12-07 64851 33 287 0
2022-12-06 64818 40 287 1
2022-12-05 64778 36 286 1
2022-12-02 64742 25 285 0
2022-12-01 64717 24 285 0
2022-11-30 64693 26 285 0
2022-11-29 64667 47 285 0
2022-11-28 64620 23 285 0
2022-11-25 64597 22 285 1
2022-11-24 64575 21 284 0
2022-11-23 64554 24 284 1
2022-11-22 64530 57 283 0
2022-11-21 64473 44 283 0
2022-11-18 64429 39 283 1
2022-11-17 64390 12 282 0
2022-11-16 64378 42 282 0
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2022-11-03 63945 98 282 1
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2022-11-01 63805 1 281 0
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2022-10-28 63749 61 281 0
2022-10-27 63688 87 281 0
2022-10-26 63601 111 281 0
2022-10-25 63490 175 281 1
2022-10-24 63315 110 280 0
2022-10-22 63205 38 280 0
2022-10-21 63167 90 280 0
2022-10-20 63077 123 280 0
2022-10-19 62954 171 280 0
2022-10-18 62783 258 280 0
2022-10-17 62525 208 280 0
2022-10-15 62317 13 280 0
2022-10-14 62304 134 280 0
2022-10-13 62170 216 280 1
2022-10-12 61954 217 279 0
2022-10-11 61737 287 279 0
2022-10-10 61450 243 279 0
2022-10-08 61207 2 279 0
2022-10-07 61205 114 279 0
2022-10-06 61091 257 279 0
2022-10-05 60834 165 279 1
2022-10-04 60669 184 278 0
2022-09-30 60485 68 278 0
2022-09-29 60417 101 278 0
2022-09-28 60316 108 278 0
2022-09-27 60208 168 278 0
2022-09-26 60040 70 278 0
2022-09-23 59970 52 278 0
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2022-08-29 59016 49 277 1
2022-08-26 58967 24 276 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: 0:02:54.084444