Germany: LK Mittelsachsen (Sachsen)¶

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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 15:27:23 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Mittelsachsen", weeks=5);
2023-03-07T15:27:39.878550 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 30 30 35 35 40 40 45 45 7-day incidence rate (per 100K people) 28.4 LK Mittelsachsen, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5 10 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 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 Mittelsachsen cases daily growth factor Germany-LK Mittelsachsen cases daily growth factor (rolling mean) Germany-LK Mittelsachsen 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 Mittelsachsen deaths daily growth factor Germany-LK Mittelsachsen deaths daily growth factor (rolling mean) Germany-LK Mittelsachsen estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 5000 10000 cases doubling time [days] Germany-LK Mittelsachsen doubling time cases (rolling mean) 0.00 14.97 29.93 daily change Germany-LK Mittelsachsen new cases (rolling 7d mean) Germany-LK Mittelsachsen new cases 0.000 0.299 0.599 0.898 daily change Germany-LK Mittelsachsen new deaths (rolling 7d mean) Germany-LK Mittelsachsen new deaths 0.000 0.375 0.751
In [4]:
overview(country="Germany", subregion="LK Mittelsachsen");
2023-03-07T15:27:55.344225 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) 28.4 LK Mittelsachsen, 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 R & growth factor (based on cases) Germany-LK Mittelsachsen cases daily growth factor Germany-LK Mittelsachsen cases daily growth factor (rolling mean) Germany-LK Mittelsachsen 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 Mittelsachsen deaths daily growth factor Germany-LK Mittelsachsen deaths daily growth factor (rolling mean) Germany-LK Mittelsachsen estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 10000 20000 30000 cases doubling time [days] Germany-LK Mittelsachsen doubling time cases (rolling mean) Germany-LK Mittelsachsen doubling time deaths (rolling mean) 0 599 1197 daily change Germany-LK Mittelsachsen new cases (rolling 7d mean) Germany-LK Mittelsachsen new cases 0.00 5.99 11.97 17.96 daily change Germany-LK Mittelsachsen new deaths (rolling 7d mean) Germany-LK Mittelsachsen new deaths 0 353 707 1060 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Mittelsachsen", dates="2020-03-15:");
2023-03-07T15:30:39.758665 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 Mittelsachsen LK Mittelsachsen 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 Mittelsachsen Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Mittelsachsen")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Mittelsachsen")
print(f'Population of country="Germany", subregion="LK Mittelsachsen": {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 Mittelsachsen": 299329 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-03 140975 16 1069 0
2023-03-02 140959 13 1069 0
2023-03-01 140946 20 1069 0
2023-02-28 140926 36 1069 0
2023-02-27 140890 34 1069 0
2023-02-24 140856 24 1069 0
2023-02-23 140832 17 1069 0
2023-02-22 140815 31 1069 0
2023-02-21 140784 25 1069 0
2023-02-20 140759 41 1069 0
2023-02-19 140718 2 1069 0
2023-02-17 140716 5 1069 0
2023-02-16 140711 19 1069 0
2023-02-15 140692 23 1069 0
2023-02-14 140669 28 1069 0
2023-02-13 140641 23 1069 0
2023-02-10 140618 14 1069 0
2023-02-09 140604 15 1069 0
2023-02-08 140589 18 1069 1
2023-02-07 140571 23 1068 0
2023-02-06 140548 21 1068 0
2023-02-03 140527 16 1068 0
2023-02-02 140511 11 1068 0
2023-02-01 140500 13 1068 0
2023-01-31 140487 25 1068 0
2023-01-30 140462 20 1068 0
2023-01-27 140442 11 1068 0
2023-01-26 140431 22 1068 0
2023-01-25 140409 15 1068 0
2023-01-24 140394 21 1068 0
2023-01-23 140373 6 1068 0
2023-01-20 140367 6 1068 0
2023-01-19 140361 11 1068 0
2023-01-18 140350 12 1068 0
2023-01-17 140338 23 1068 0
2023-01-16 140315 22 1068 0
2023-01-13 140293 13 1068 0
2023-01-12 140280 16 1068 1
2023-01-11 140264 24 1067 0
2023-01-10 140240 28 1067 0
2023-01-09 140212 55 1067 0
2023-01-06 140157 31 1067 0
2023-01-05 140126 48 1067 0
2023-01-04 140078 84 1067 2
2023-01-03 139994 19 1065 0
2023-01-02 139975 62 1065 1
2022-12-30 139913 35 1064 2
2022-12-29 139878 50 1062 0
2022-12-28 139828 60 1062 1
2022-12-27 139768 94 1061 0
2022-12-25 139674 1 1061 0
2022-12-24 139673 2 1061 0
2022-12-23 139671 44 1061 0
2022-12-22 139627 70 1061 0
2022-12-21 139557 92 1061 0
2022-12-20 139465 213 1061 1
2022-12-17 139252 1 1060 0
2022-12-16 139251 76 1060 0
2022-12-15 139175 72 1060 1
2022-12-14 139103 93 1059 2
2022-12-13 139010 106 1057 0
2022-12-12 138904 121 1057 1
2022-12-09 138783 58 1056 0
2022-12-08 138725 88 1056 0
2022-12-07 138637 129 1056 0
2022-12-06 138508 86 1056 0
2022-12-05 138422 106 1056 1
2022-12-02 138316 69 1055 1
2022-12-01 138247 74 1054 0
2022-11-30 138173 87 1054 0
2022-11-29 138086 143 1054 0
2022-11-28 137943 109 1054 0
2022-11-25 137834 75 1054 0
2022-11-24 137759 64 1054 0
2022-11-23 137695 65 1054 0
2022-11-22 137630 90 1054 0
2022-11-21 137540 99 1054 0
2022-11-18 137441 61 1054 0
2022-11-17 137380 104 1054 0
2022-11-15 137276 60 1054 0
2022-11-14 137216 81 1054 0
2022-11-11 137135 61 1054 0
2022-11-10 137074 70 1054 0
2022-11-09 137004 86 1054 0
2022-11-08 136918 128 1054 0
2022-11-07 136790 98 1054 1
2022-11-04 136692 61 1053 0
2022-11-03 136631 139 1053 0
2022-11-02 136492 216 1053 1
2022-11-01 136276 184 1052 0
2022-10-28 136092 110 1052 0
2022-10-27 135982 144 1052 0
2022-10-26 135838 190 1052 0
2022-10-25 135648 305 1052 1
2022-10-24 135343 224 1051 1
2022-10-21 135119 198 1050 0
2022-10-20 134921 235 1050 0
2022-10-19 134686 376 1050 0
2022-10-18 134310 350 1050 0
2022-10-17 133960 408 1050 1
2022-10-14 133552 242 1049 0
2022-10-13 133310 318 1049 1
2022-10-12 132992 435 1048 1
2022-10-11 132557 476 1047 1
2022-10-10 132081 567 1046 0
2022-10-07 131514 324 1046 0
2022-10-06 131190 534 1046 1
2022-10-05 130656 499 1045 0
2022-10-04 130157 358 1045 1
2022-10-03 129799 1 1044 0
2022-09-30 129798 271 1044 0
2022-09-29 129527 231 1044 0
2022-09-28 129296 332 1044 0
2022-09-27 128964 475 1044 0
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2022-09-21 127777 194 1044 0
2022-09-20 127583 296 1044 0
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2022-09-15 126959 282 1044 0
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2022-09-11 126228 1 1043 0
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2022-08-24 124797 60 1041 0
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2022-08-22 124538 176 1041 2
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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: 0:04:33.786027