Germany: SK Delmenhorst (Niedersachsen)¶

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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 13:39:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Delmenhorst", weeks=5);
2023-03-07T13:39:11.157817 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) 65.8 SK Delmenhorst, 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 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 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-SK Delmenhorst cases daily growth factor Germany-SK Delmenhorst cases daily growth factor (rolling mean) Germany-SK Delmenhorst 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-SK Delmenhorst deaths daily growth factor Germany-SK Delmenhorst deaths daily growth factor (rolling mean) Germany-SK Delmenhorst estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 cases doubling time [days] Germany-SK Delmenhorst doubling time cases (rolling mean) 0.00 7.75 15.50 23.26 31.01 daily change Germany-SK Delmenhorst new cases (rolling 7d mean) Germany-SK Delmenhorst new cases 0.000 0.388 0.775 daily change Germany-SK Delmenhorst new deaths (rolling 7d mean) Germany-SK Delmenhorst new deaths 0.000 0.367 0.734
In [4]:
overview(country="Germany", subregion="SK Delmenhorst");
2023-03-07T13:39:26.164840 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 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 7-day incidence rate (per 100K people) 65.8 SK Delmenhorst, Germany, last data point from 2023-03-06 Jan 20 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 Jan 20 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 Jan 20 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-SK Delmenhorst cases daily growth factor Germany-SK Delmenhorst cases daily growth factor (rolling mean) Germany-SK Delmenhorst estimated R (using cases) Jan 20 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-SK Delmenhorst deaths daily growth factor Germany-SK Delmenhorst deaths daily growth factor (rolling mean) Germany-SK Delmenhorst estimated R (using deaths) Jan 20 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-SK Delmenhorst doubling time cases (rolling mean) Germany-SK Delmenhorst doubling time deaths (rolling mean) 0.0 77.5 155.0 232.6 310.1 daily change Germany-SK Delmenhorst new cases (rolling 7d mean) Germany-SK Delmenhorst new cases 0.000 1.550 3.101 daily change Germany-SK Delmenhorst new deaths (rolling 7d mean) Germany-SK Delmenhorst new deaths 0.0 22.4 44.8 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Delmenhorst", dates="2020-03-15:");
2023-03-07T13:41:23.648306 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: SK Delmenhorst SK Delmenhorst 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 SK Delmenhorst Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Delmenhorst")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Delmenhorst")
print(f'Population of country="Germany", subregion="SK Delmenhorst": {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="SK Delmenhorst": 77522 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 37205 11 133 0
2023-03-03 37194 3 133 0
2023-03-02 37191 8 133 0
2023-03-01 37183 29 133 0
2023-02-27 37154 19 133 0
2023-02-24 37135 10 133 0
2023-02-23 37125 23 133 0
2023-02-22 37102 9 133 0
2023-02-21 37093 21 133 0
2023-02-20 37072 26 133 1
2023-02-17 37046 22 132 0
2023-02-16 37024 13 132 0
2023-02-15 37011 14 132 0
2023-02-14 36997 23 132 0
2023-02-13 36974 38 132 0
2023-02-10 36936 7 132 0
2023-02-09 36929 5 132 0
2023-02-08 36924 12 132 0
2023-02-07 36912 17 132 0
2023-02-06 36895 21 132 0
2023-02-03 36874 11 132 0
2023-02-02 36863 24 132 0
2023-02-01 36839 11 132 0
2023-01-31 36828 22 132 0
2023-01-30 36806 28 132 0
2023-01-27 36778 13 132 0
2023-01-26 36765 16 132 0
2023-01-25 36749 14 132 0
2023-01-24 36735 29 132 0
2023-01-23 36706 20 132 0
2023-01-20 36686 7 132 0
2023-01-19 36679 16 132 0
2023-01-18 36663 8 132 0
2023-01-17 36655 32 132 0
2023-01-16 36623 34 132 1
2023-01-13 36589 26 131 1
2023-01-12 36563 13 130 0
2023-01-11 36550 20 130 0
2023-01-10 36530 31 130 0
2023-01-09 36499 53 130 0
2023-01-06 36446 28 130 0
2023-01-05 36418 26 130 0
2023-01-04 36392 58 130 1
2023-01-03 36334 43 129 0
2023-01-02 36291 52 129 1
2022-12-30 36239 40 128 1
2022-12-29 36199 50 127 0
2022-12-28 36149 103 127 0
2022-12-27 36046 80 127 0
2022-12-26 35966 1 127 0
2022-12-23 35965 49 127 0
2022-12-22 35916 57 127 0
2022-12-21 35859 82 127 0
2022-12-20 35777 188 127 0
2022-12-19 35589 62 127 0
2022-12-16 35527 43 127 1
2022-12-15 35484 69 126 0
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2021-04-21 2943 39 64 0
2021-04-20 2904 7 64 0
2021-04-19 2897 25 64 1
2021-04-17 2872 26 63 0
2021-04-16 2846 30 63 0
2021-04-15 2816 37 63 0
2021-04-14 2779 34 63 0
2021-04-13 2745 10 63 0
2021-04-12 2735 11 63 0
2021-04-10 2724 16 63 1
2021-04-09 2708 19 62 0
2021-04-08 2689 26 62 0
2021-04-07 2663 6 62 0
2021-04-06 2657 11 62 0
2021-04-04 2646 13 62 1
2021-04-02 2633 22 61 0
2021-04-01 2611 16 61 0
2021-03-31 2595 26 61 0
2021-03-30 2569 7 61 0
2021-03-29 2562 17 61 0
2021-03-27 2545 30 61 0
2021-03-26 2515 17 61 0
2021-03-25 2498 9 61 0
2021-03-24 2489 28 61 0
2021-03-23 2461 4 61 0
2021-03-22 2457 14 61 0
2021-03-20 2443 8 61 0
2021-03-19 2435 16 61 0
2021-03-18 2419 11 61 1
2021-03-17 2408 6 60 0
2021-03-16 2402 5 60 0
2021-03-15 2397 8 60 1
2021-03-13 2389 9 59 0
2021-03-12 2380 8 59 1
2021-03-11 2372 13 58 0
2021-03-10 2359 23 58 0
2021-03-09 2336 8 58 1
2021-03-08 2328 2 57 0
2021-03-06 2326 9 57 0
2021-03-05 2317 7 57 0
2021-03-04 2310 7 57 0
2021-03-03 2303 12 57 0
2021-03-02 2291 4 57 0
2021-03-01 2287 10 57 0
2021-02-27 2277 5 57 0
2021-02-26 2272 14 57 0
2021-02-25 2258 9 57 2
2021-02-24 2249 14 55 0
2021-02-23 2235 8 55 0
2021-02-22 2227 5 55 0
2021-02-20 2222 3 55 0
2021-02-19 2219 6 55 0
2021-02-18 2213 4 55 0
2021-02-17 2209 6 55 0
2021-02-16 2203 3 55 0
2021-02-15 2200 6 55 0
2021-02-13 2194 6 55 0
2021-02-12 2188 8 55 0
2021-02-11 2180 6 55 0
2021-02-10 2174 12 55 0
2021-02-09 2162 2 55 1
2021-02-08 2160 4 54 0
2021-02-06 2156 15 54 0
2021-02-05 2141 8 54 1
2021-02-04 2133 6 53 1
2021-02-03 2127 8 52 1
2021-02-02 2119 7 51 0
2021-02-01 2112 10 51 0
2021-01-30 2102 10 51 1
2021-01-29 2092 22 50 1
2021-01-28 2070 10 49 0
2021-01-27 2060 12 49 0
2021-01-26 2048 7 49 1
2021-01-25 2041 5 48 0
2021-01-23 2036 14 48 0
2021-01-22 2022 3 48 0
2021-01-21 2019 5 48 0
2021-01-20 2014 10 48 0
2021-01-19 2004 7 48 0
2021-01-18 1997 4 48 0
2021-01-17 1993 1 48 0
2021-01-16 1992 8 48 0
2021-01-15 1984 13 48 2
2021-01-14 1971 10 46 0
2021-01-13 1961 13 46 1
2021-01-12 1948 9 45 1
2021-01-11 1939 12 44 0
2021-01-09 1927 9 44 0
2021-01-08 1918 22 44 0
2021-01-07 1896 8 44 0
2021-01-06 1888 32 44 0
2021-01-05 1856 7 44 0
2021-01-04 1849 35 44 3
2020-12-31 1814 19 41 0
2020-12-30 1795 61 41 4
2020-12-29 1734 17 37 4
2020-12-28 1717 17 33 1
2020-12-24 1700 17 32 0
2020-12-23 1683 27 32 0
2020-12-22 1656 7 32 1
2020-12-21 1649 10 31 0
2020-12-19 1639 9 31 0
2020-12-18 1630 19 31 1
2020-12-17 1611 20 30 0
2020-12-16 1591 52 30 1
2020-12-15 1539 18 29 1
2020-12-14 1521 20 28 1
2020-12-12 1501 13 27 1
2020-12-11 1488 22 26 0
2020-12-10 1466 21 26 0
2020-12-09 1445 22 26 0
2020-12-08 1423 7 26 1
2020-12-07 1416 14 25 0
2020-12-05 1402 13 25 0
2020-12-04 1389 22 25 0
2020-12-03 1367 29 25 0
2020-12-02 1338 47 25 0
2020-12-01 1291 15 25 0
2020-11-30 1276 18 25 1
2020-11-28 1258 6 24 0
2020-11-27 1252 17 24 1
2020-11-26 1235 16 23 0
2020-11-25 1219 23 23 2
2020-11-24 1196 9 21 0
2020-11-23 1187 8 21 0
2020-11-21 1179 26 21 1
2020-11-20 1153 13 20 1
2020-11-19 1140 45 19 3
2020-11-18 1095 20 16 0
2020-11-17 1075 6 16 0
2020-11-16 1069 6 16 0
2020-11-15 1063 3 16 0
2020-11-14 1060 12 16 0
2020-11-13 1048 12 16 0
2020-11-12 1036 21 16 1
2020-11-11 1015 47 15 3
2020-11-10 968 10 12 0
2020-11-09 958 2 12 0
2020-11-08 956 15 12 0
2020-11-07 941 28 12 1
2020-11-06 913 12 11 0
2020-11-05 901 39 11 1
2020-11-04 862 45 10 0
2020-11-03 817 15 10 0
2020-11-02 802 4 10 1
2020-11-01 798 11 9 0
2020-10-31 787 36 9 0
2020-10-30 751 28 9 0
2020-10-29 723 36 9 0
2020-10-28 687 20 9 0
2020-10-27 667 19 9 1
2020-10-26 648 3 8 0
2020-10-25 645 12 8 2
2020-10-24 633 40 6 0
2020-10-23 593 15 6 0
2020-10-22 578 69 6 0
2020-10-21 509 45 6 0
2020-10-20 464 3 6 0
2020-10-19 461 4 6 0
2020-10-18 457 74 6 0
2020-10-15 383 21 6 0
2020-10-14 362 63 6 2
2020-10-13 299 7 4 0
2020-10-12 292 9 4 0
2020-10-11 283 8 4 0
2020-10-10 275 13 4 0
2020-10-09 262 17 4 0
2020-10-08 245 18 4 0
2020-10-07 227 15 4 0
2020-10-06 212 12 4 0
2020-10-03 200 2 4 0
2020-10-02 198 2 4 0
2020-10-01 196 6 4 0
2020-09-30 190 6 4 0
2020-09-29 184 2 4 0
2020-09-28 182 3 4 0
2020-09-23 179 3 4 0
2020-09-18 176 1 4 0
2020-09-16 175 1 4 0
2020-09-15 174 1 4 0
2020-09-13 173 4 4 0
2020-09-10 169 4 4 0
2020-09-09 165 5 4 0
2020-09-08 160 1 4 0
2020-09-07 159 4 4 0
2020-09-02 155 6 4 0
2020-08-31 149 1 4 0
2020-08-29 148 1 4 0
2020-08-26 147 2 4 0
2020-08-24 145 3 4 0
2020-08-18 142 5 4 0
2020-08-16 137 2 4 1
2020-08-14 135 3 3 0
2020-08-12 132 3 3 0
2020-08-05 129 1 3 0
2020-08-04 128 1 3 0
2020-07-21 127 3 3 0
2020-07-10 124 1 3 0
2020-07-09 123 1 3 0
2020-07-08 122 5 3 0
2020-07-07 117 2 3 0
2020-07-01 115 2 3 0
2020-06-30 113 1 3 0
2020-06-28 112 5 3 0
2020-06-27 107 1 3 0
2020-06-26 106 7 3 0
2020-06-25 99 2 3 0
2020-06-24 97 2 3 0
2020-06-23 95 8 3 0
2020-06-17 87 2 3 0
2020-06-10 85 1 3 0
2020-06-08 84 2 3 0
2020-06-05 82 1 3 0
2020-06-04 81 6 3 0
2020-06-03 75 6 3 0
2020-05-29 69 3 3 0
2020-05-28 66 1 3 0
2020-05-27 65 3 3 0
2020-05-21 62 1 3 0
2020-05-19 61 7 3 0
2020-05-13 54 1 3 0
2020-05-12 53 1 3 0
2020-05-09 52 1 3 1
2020-05-08 51 2 2 0
2020-05-05 49 2 2 0
2020-05-01 47 1 2 0
2020-04-27 46 1 2 0
2020-04-26 45 3 2 0
2020-04-23 42 1 2 0
2020-04-22 41 3 2 0
2020-04-19 38 3 2 0
2020-04-18 35 1 2 0
2020-04-16 34 1 2 0
2020-04-15 33 1 2 0
2020-04-13 32 2 2 0
2020-04-09 30 1 2 0
2020-04-08 29 2 2 0
2020-04-04 27 1 2 0
2020-04-03 26 1 2 1
2020-04-02 25 1 1 0
2020-03-31 24 3 1 0
2020-03-30 21 1 1 0
2020-03-29 20 2 1 0
2020-03-27 18 1 1 1
2020-03-26 17 1 0 0
2020-03-25 16 2 0 0
2020-03-23 14 4 0 0
2020-03-19 10 3 0 0
2020-03-17 7 1 0 0
2020-03-16 6 1 0 0
2020-03-11 5 4 0 0

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: 0:02:33.807368