Germany: LK Erlangen-Höchstadt (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 10:47:26 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Erlangen-Höchstadt", weeks=5);
2023-03-07T10:47:38.444604 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) 80.4 LK Erlangen-Höchstadt, 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 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 Erlangen-Höchstadt cases daily growth factor Germany-LK Erlangen-Höchstadt cases daily growth factor (rolling mean) Germany-LK Erlangen-Höchstadt 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 Erlangen-Höchstadt deaths daily growth factor Germany-LK Erlangen-Höchstadt deaths daily growth factor (rolling mean) Germany-LK Erlangen-Höchstadt 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 Erlangen-Höchstadt doubling time cases (rolling mean) 0.00 27.86 55.73 daily change Germany-LK Erlangen-Höchstadt new cases (rolling 7d mean) Germany-LK Erlangen-Höchstadt new cases 0.000 0.279 0.557 0.836 daily change Germany-LK Erlangen-Höchstadt new deaths (rolling 7d mean) Germany-LK Erlangen-Höchstadt new deaths 0.000 0.389 0.778
In [4]:
overview(country="Germany", subregion="LK Erlangen-Höchstadt");
2023-03-07T10:47:57.034137 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 3000 3000 7-day incidence rate (per 100K people) 80.4 LK Erlangen-Höchstadt, 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 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 R & growth factor (based on cases) Germany-LK Erlangen-Höchstadt cases daily growth factor Germany-LK Erlangen-Höchstadt cases daily growth factor (rolling mean) Germany-LK Erlangen-Höchstadt 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 Erlangen-Höchstadt deaths daily growth factor Germany-LK Erlangen-Höchstadt deaths daily growth factor (rolling mean) Germany-LK Erlangen-Höchstadt 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 Erlangen-Höchstadt doubling time cases (rolling mean) Germany-LK Erlangen-Höchstadt doubling time deaths (rolling mean) 0 279 557 836 daily change Germany-LK Erlangen-Höchstadt new cases (rolling 7d mean) Germany-LK Erlangen-Höchstadt new cases 0.00 2.79 5.57 8.36 daily change Germany-LK Erlangen-Höchstadt new deaths (rolling 7d mean) Germany-LK Erlangen-Höchstadt new deaths 0.0 31.3 62.6 93.9 125.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Erlangen-Höchstadt", dates="2020-03-15:");
2023-03-07T10:50:32.189136 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 Erlangen-Höchstadt LK Erlangen-Höchstadt 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 Erlangen-Höchstadt Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Erlangen-Höchstadt")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Erlangen-Höchstadt")
print(f'Population of country="Germany", subregion="LK Erlangen-Höchstadt": {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 Erlangen-Höchstadt": 139323 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 72706 15 202 0
2023-03-03 72691 12 202 0
2023-03-02 72679 17 202 0
2023-03-01 72662 28 202 0
2023-02-28 72634 40 202 0
2023-02-27 72594 52 202 1
2023-02-26 72542 1 201 0
2023-02-24 72541 34 201 0
2023-02-23 72507 38 201 0
2023-02-22 72469 29 201 0
2023-02-21 72440 43 201 0
2023-02-20 72397 70 201 0
2023-02-17 72327 25 201 0
2023-02-16 72302 29 201 0
2023-02-15 72273 55 201 0
2023-02-14 72218 70 201 0
2023-02-13 72148 60 201 0
2023-02-11 72088 2 201 0
2023-02-10 72086 26 201 0
2023-02-09 72060 43 201 0
2023-02-08 72017 54 201 0
2023-02-07 71963 63 201 0
2023-02-06 71900 45 201 0
2023-02-03 71855 26 201 0
2023-02-02 71829 31 201 1
2023-02-01 71798 34 200 1
2023-01-31 71764 47 199 0
2023-01-30 71717 44 199 0
2023-01-27 71673 30 199 0
2023-01-26 71643 25 199 0
2023-01-25 71618 22 199 0
2023-01-24 71596 32 199 0
2023-01-23 71564 33 199 0
2023-01-20 71531 15 199 0
2023-01-19 71516 22 199 0
2023-01-18 71494 19 199 0
2023-01-17 71475 25 199 0
2023-01-16 71450 34 199 0
2023-01-14 71416 1 199 0
2023-01-13 71415 18 199 0
2023-01-12 71397 23 199 0
2023-01-11 71374 27 199 0
2023-01-10 71347 47 199 0
2023-01-09 71300 51 199 1
2023-01-08 71249 1 198 0
2023-01-06 71248 1 198 0
2023-01-05 71247 21 198 1
2023-01-04 71226 43 197 0
2023-01-03 71183 43 197 0
2023-01-02 71140 54 197 0
2022-12-30 71086 24 197 0
2022-12-29 71062 34 197 0
2022-12-28 71028 45 197 0
2022-12-27 70983 81 197 0
2022-12-24 70902 3 197 0
2022-12-23 70899 52 197 0
2022-12-22 70847 57 197 0
2022-12-21 70790 44 197 0
2022-12-20 70746 63 197 0
2022-12-19 70683 67 197 0
2022-12-17 70616 3 197 0
2022-12-16 70613 28 197 0
2022-12-15 70585 22 197 0
2022-12-14 70563 45 197 0
2022-12-13 70518 39 197 0
2022-12-12 70479 60 197 0
2022-12-11 70419 1 197 0
2022-12-09 70418 40 197 0
2022-12-08 70378 20 197 0
2022-12-07 70358 30 197 0
2022-12-06 70328 63 197 0
2022-12-05 70265 59 197 0
2022-12-02 70206 36 197 0
2022-12-01 70170 55 197 0
2022-11-30 70115 19 197 0
2022-11-29 70096 35 197 0
2022-11-28 70061 30 197 0
2022-11-26 70031 1 197 0
2022-11-25 70030 24 197 0
2022-11-24 70006 19 197 0
2022-11-23 69987 27 197 0
2022-11-22 69960 57 197 0
2022-11-21 69903 43 197 0
2022-11-18 69860 36 197 0
2022-11-17 69824 64 197 0
2022-11-16 69760 1 197 0
2022-11-15 69759 45 197 0
2022-11-14 69714 53 197 0
2022-11-13 69661 1 197 0
2022-11-11 69660 33 197 0
2022-11-10 69627 42 197 0
2022-11-09 69585 68 197 0
2022-11-08 69517 64 197 0
2022-11-07 69453 75 197 0
2022-11-04 69378 41 197 0
2022-11-03 69337 55 197 0
2022-11-02 69282 93 197 0
2022-11-01 69189 1 197 0
2022-10-31 69188 127 197 0
2022-10-28 69061 64 197 1
2022-10-27 68997 75 196 0
2022-10-26 68922 92 196 0
2022-10-25 68830 149 196 0
2022-10-24 68681 199 196 0
2022-10-21 68482 115 196 0
2022-10-20 68367 129 196 0
2022-10-19 68238 177 196 0
2022-10-18 68061 357 196 1
2022-10-17 67704 306 195 0
2022-10-16 67398 1 195 0
2022-10-15 67397 5 195 0
2022-10-14 67392 134 195 0
2022-10-13 67258 224 195 0
2022-10-12 67034 219 195 1
2022-10-11 66815 238 194 1
2022-10-10 66577 359 193 2
2022-10-09 66218 1 191 0
2022-10-08 66217 2 191 0
2022-10-07 66215 251 191 0
2022-10-06 65964 299 191 0
2022-10-05 65665 262 191 0
2022-10-04 65403 355 191 0
2022-10-01 65048 3 191 0
2022-09-30 65045 135 191 0
2022-09-29 64910 150 191 1
2022-09-28 64760 169 190 0
2022-09-27 64591 211 190 0
2022-09-26 64380 207 190 0
2022-09-25 64173 2 190 0
2022-09-24 64171 1 190 0
2022-09-23 64170 93 190 0
2022-09-22 64077 91 190 0
2022-09-21 63986 83 190 0
2022-09-20 63903 112 190 0
2022-09-19 63791 106 190 0
2022-09-16 63685 107 190 0
2022-09-15 63578 52 190 0
2022-09-14 63526 66 190 0
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2022-09-12 63365 84 190 0
2022-09-09 63281 43 190 0
2022-09-08 63238 44 190 0
2022-09-07 63194 40 190 0
2022-09-06 63154 69 190 0
2022-09-05 63085 63 190 0
2022-09-02 63022 54 190 0
2022-09-01 62968 61 190 0
2022-08-31 62907 44 190 0
2022-08-30 62863 67 190 0
2022-08-29 62796 105 190 0
2022-08-26 62691 25 190 0
2022-08-25 62666 41 190 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: 0:03:21.126227