Germany: LK Görlitz (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 15:20:23 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Görlitz", weeks=5);
2023-03-07T15:20:33.282480 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 50 50 60 60 7-day incidence rate (per 100K people) 34.2 LK Görlitz, 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 15 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 0.4 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 Görlitz cases daily growth factor Germany-LK Görlitz cases daily growth factor (rolling mean) Germany-LK Görlitz 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 Görlitz deaths daily growth factor Germany-LK Görlitz deaths daily growth factor (rolling mean) Germany-LK Görlitz estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Görlitz doubling time cases (rolling mean) 0.00 12.41 24.83 37.24 daily change Germany-LK Görlitz new cases (rolling 7d mean) Germany-LK Görlitz new cases 0.000 0.248 0.497 0.745 0.993 daily change Germany-LK Görlitz new deaths (rolling 7d mean) Germany-LK Görlitz new deaths 0.000 0.214 0.428 0.642 0.857
In [4]:
overview(country="Germany", subregion="LK Görlitz");
2023-03-07T15:20:48.679381 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 34.2 LK Görlitz, 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 0 100 200 300 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 2.5 5.0 7.5 10.0 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 Görlitz cases daily growth factor Germany-LK Görlitz cases daily growth factor (rolling mean) Germany-LK Görlitz 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 Görlitz deaths daily growth factor Germany-LK Görlitz deaths daily growth factor (rolling mean) Germany-LK Görlitz 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 Görlitz doubling time cases (rolling mean) Germany-LK Görlitz doubling time deaths (rolling mean) 0 248 497 745 993 daily change Germany-LK Görlitz new cases (rolling 7d mean) Germany-LK Görlitz new cases 0.00 6.21 12.41 18.62 24.83 daily change Germany-LK Görlitz new deaths (rolling 7d mean) Germany-LK Görlitz new deaths 0 436 872 1309 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Görlitz", dates="2020-03-15:");
2023-03-07T15:22:55.036760 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 Görlitz LK Görlitz 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 10 10 daily new deaths (rolling 7-day mean) normalised by 100K people LK Görlitz Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Görlitz")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Görlitz")
print(f'Population of country="Germany", subregion="LK Görlitz": {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 Görlitz": 248273 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 111168 1 1745 0
2023-03-04 111167 9 1745 0
2023-03-03 111158 17 1745 0
2023-03-02 111141 20 1745 0
2023-03-01 111121 13 1745 0
2023-02-28 111108 25 1745 0
2023-02-27 111083 32 1745 0
2023-02-26 111051 8 1745 0
2023-02-25 111043 12 1745 0
2023-02-24 111031 30 1745 0
2023-02-23 111001 16 1745 0
2023-02-22 110985 26 1745 0
2023-02-21 110959 29 1745 0
2023-02-20 110930 38 1745 0
2023-02-19 110892 6 1745 0
2023-02-18 110886 6 1745 0
2023-02-17 110880 24 1745 0
2023-02-16 110856 29 1745 0
2023-02-15 110827 8 1745 0
2023-02-14 110819 31 1745 0
2023-02-13 110788 21 1745 1
2023-02-12 110767 4 1744 0
2023-02-11 110763 5 1744 0
2023-02-10 110758 15 1744 0
2023-02-09 110743 15 1744 0
2023-02-08 110728 12 1744 0
2023-02-07 110716 14 1744 0
2023-02-06 110702 27 1744 0
2023-02-05 110675 6 1744 0
2023-02-04 110669 4 1744 0
2023-02-03 110665 8 1744 0
2023-02-02 110657 18 1744 0
2023-02-01 110639 18 1744 0
2023-01-31 110621 25 1744 0
2023-01-30 110596 30 1744 0
2023-01-29 110566 4 1744 0
2023-01-28 110562 9 1744 0
2023-01-27 110553 17 1744 0
2023-01-26 110536 13 1744 0
2023-01-25 110523 14 1744 0
2023-01-24 110509 19 1744 0
2023-01-23 110490 15 1744 0
2023-01-22 110475 7 1744 0
2023-01-21 110468 5 1744 0
2023-01-20 110463 12 1744 0
2023-01-19 110451 12 1744 0
2023-01-18 110439 9 1744 1
2023-01-17 110430 31 1743 0
2023-01-16 110399 21 1743 0
2023-01-15 110378 3 1743 0
2023-01-14 110375 13 1743 0
2023-01-13 110362 13 1743 0
2023-01-12 110349 15 1743 1
2023-01-11 110334 14 1742 0
2023-01-10 110320 25 1742 1
2023-01-09 110295 47 1741 3
2023-01-08 110248 5 1738 0
2023-01-07 110243 6 1738 1
2023-01-06 110237 25 1737 0
2023-01-05 110212 25 1737 0
2023-01-04 110187 28 1737 0
2023-01-03 110159 77 1737 0
2023-01-02 110082 24 1737 0
2023-01-01 110058 5 1737 0
2022-12-31 110053 14 1737 1
2022-12-30 110039 27 1736 0
2022-12-29 110012 37 1736 2
2022-12-28 109975 40 1734 2
2022-12-27 109935 70 1732 0
2022-12-26 109865 15 1732 0
2022-12-25 109850 4 1732 0
2022-12-24 109846 10 1732 0
2022-12-23 109836 43 1732 0
2022-12-22 109793 39 1732 1
2022-12-21 109754 87 1731 0
2022-12-20 109667 34 1731 0
2022-12-19 109633 63 1731 0
2022-12-18 109570 9 1731 0
2022-12-17 109561 18 1731 1
2022-12-16 109543 40 1730 1
2022-12-15 109503 56 1729 0
2022-12-14 109447 58 1729 0
2022-12-13 109389 75 1729 3
2022-12-12 109314 72 1726 1
2022-12-11 109242 13 1725 0
2022-12-10 109229 18 1725 1
2022-12-09 109211 53 1724 0
2022-12-08 109158 52 1724 0
2022-12-07 109106 55 1724 0
2022-12-06 109051 56 1724 0
2022-12-05 108995 83 1724 1
2022-12-04 108912 4 1723 0
2022-12-03 108908 6 1723 1
2022-12-02 108902 29 1722 0
2022-12-01 108873 38 1722 0
2022-11-30 108835 39 1722 0
2022-11-29 108796 74 1722 0
2022-11-28 108722 81 1722 0
2022-11-27 108641 13 1722 0
2022-11-26 108628 11 1722 1
2022-11-25 108617 42 1721 1
2022-11-24 108575 39 1720 0
2022-11-23 108536 35 1720 0
2022-11-22 108501 66 1720 2
2022-11-21 108435 73 1718 1
2022-11-20 108362 4 1717 0
2022-11-19 108358 8 1717 0
2022-11-18 108350 40 1717 1
2022-11-17 108310 67 1716 1
2022-11-16 108243 9 1715 0
2022-11-15 108234 59 1715 0
2022-11-14 108175 66 1715 3
2022-11-13 108109 11 1712 0
2022-11-12 108098 19 1712 2
2022-11-11 108079 52 1710 0
2022-11-10 108027 60 1710 0
2022-11-09 107967 59 1710 2
2022-11-08 107908 77 1708 0
2022-11-07 107831 101 1708 2
2022-11-06 107730 12 1706 1
2022-11-05 107718 22 1705 0
2022-11-04 107696 66 1705 0
2022-11-03 107630 70 1705 1
2022-11-02 107560 74 1704 0
2022-11-01 107486 134 1704 0
2022-10-31 107352 56 1704 0
2022-10-30 107296 17 1704 2
2022-10-29 107279 20 1702 0
2022-10-28 107259 84 1702 0
2022-10-27 107175 114 1702 2
2022-10-26 107061 90 1700 0
2022-10-25 106971 142 1700 1
2022-10-24 106829 195 1699 1
2022-10-23 106634 14 1698 1
2022-10-22 106620 25 1697 1
2022-10-21 106595 131 1696 0
2022-10-20 106464 147 1696 1
2022-10-19 106317 163 1695 0
2022-10-18 106154 233 1695 2
2022-10-17 105921 382 1693 2
2022-10-16 105539 10 1691 0
2022-10-15 105529 69 1691 3
2022-10-14 105460 198 1688 0
2022-10-13 105262 203 1688 2
2022-10-12 105059 244 1686 0
2022-10-11 104815 378 1686 3
2022-10-10 104437 455 1683 1
2022-10-09 103982 16 1682 0
2022-10-08 103966 95 1682 0
2022-10-07 103871 300 1682 0
2022-10-06 103571 283 1682 1
2022-10-05 103288 394 1681 3
2022-10-04 102894 471 1678 1
2022-10-03 102423 28 1677 0
2022-10-02 102395 9 1677 0
2022-10-01 102386 49 1677 0
2022-09-30 102337 196 1677 0
2022-09-29 102141 207 1677 0
2022-09-28 101934 196 1677 1
2022-09-27 101738 288 1676 0
2022-09-26 101450 330 1676 0
2022-09-25 101120 16 1676 0
2022-09-24 101104 52 1676 0
2022-09-23 101052 153 1676 0
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2022-09-19 100433 218 1675 1
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2022-09-17 100188 32 1674 0
2022-09-16 100156 121 1674 0
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2022-09-14 99889 28 1674 0
2022-09-13 99861 105 1674 0
2022-09-12 99756 180 1674 1
2022-09-11 99576 17 1673 0
2022-09-10 99559 21 1673 0
2022-09-09 99538 116 1673 1
2022-09-08 99422 74 1672 0
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2022-09-05 99127 122 1671 2
2022-09-04 99005 6 1669 0
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2022-09-02 98971 73 1669 0
2022-09-01 98898 69 1669 0
2022-08-31 98829 91 1669 0
2022-08-30 98738 89 1669 0
2022-08-29 98649 131 1669 0
2022-08-28 98518 6 1669 0
2022-08-27 98512 31 1669 0
2022-08-26 98481 79 1669 0
2022-08-25 98402 74 1669 0
2022-08-24 98328 60 1669 0
2022-08-23 98268 91 1669 0
2022-08-22 98177 129 1669 0
2022-08-21 98048 8 1669 0
2022-08-20 98040 16 1669 0
2022-08-19 98024 119 1669 0
2022-08-18 97905 45 1669 1
2022-08-17 97860 110 1668 0
2022-08-16 97750 140 1668 2
2022-08-15 97610 191 1666 0
2022-08-14 97419 18 1666 1
2022-08-13 97401 41 1665 0
2022-08-12 97360 89 1665 1
2022-08-11 97271 107 1664 0
2022-08-10 97164 107 1664 0
2022-08-09 97057 130 1664 0
2022-08-08 96927 192 1664 0
2022-08-07 96735 7 1664 0
2022-08-06 96728 35 1664 0
2022-08-05 96693 128 1664 0
2022-08-04 96565 99 1664 3
2022-08-03 96466 123 1661 0
2022-08-02 96343 175 1661 2
2022-08-01 96168 182 1659 0
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2022-07-30 95983 37 1658 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: 0:02:40.896261