Germany: LK Heidekreis (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 13:14:44 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Heidekreis", weeks=5);
2023-03-07T13:14:57.628668 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) 76.3 LK Heidekreis, 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 0.2 0.4 0.6 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Heidekreis cases daily growth factor Germany-LK Heidekreis cases daily growth factor (rolling mean) Germany-LK Heidekreis 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 Heidekreis deaths daily growth factor Germany-LK Heidekreis deaths daily growth factor (rolling mean) Germany-LK Heidekreis 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 Heidekreis doubling time cases (rolling mean) Germany-LK Heidekreis doubling time deaths (rolling mean) 0.00 14.29 28.58 42.87 57.16 daily change Germany-LK Heidekreis new cases (rolling 7d mean) Germany-LK Heidekreis new cases 0.000 0.286 0.572 0.857 daily change Germany-LK Heidekreis new deaths (rolling 7d mean) Germany-LK Heidekreis new deaths 0.0 56.1 112.3 168.4 224.6 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Heidekreis");
2023-03-07T13:15:18.385041 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) 76.3 LK Heidekreis, 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 1 2 3 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Heidekreis cases daily growth factor Germany-LK Heidekreis cases daily growth factor (rolling mean) Germany-LK Heidekreis 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 Heidekreis deaths daily growth factor Germany-LK Heidekreis deaths daily growth factor (rolling mean) Germany-LK Heidekreis 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 cases doubling time [days] Germany-LK Heidekreis doubling time cases (rolling mean) Germany-LK Heidekreis doubling time deaths (rolling mean) 0.0 142.9 285.8 428.7 571.6 daily change Germany-LK Heidekreis new cases (rolling 7d mean) Germany-LK Heidekreis new cases 0.000 1.429 2.858 4.287 daily change Germany-LK Heidekreis new deaths (rolling 7d mean) Germany-LK Heidekreis new deaths 0.0 107.2 214.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Heidekreis", dates="2020-03-15:");
2023-03-07T13:17:56.287989 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 Heidekreis LK Heidekreis 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 Heidekreis Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Heidekreis")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Heidekreis")
print(f'Population of country="Germany", subregion="LK Heidekreis": {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 Heidekreis": 142912 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 62234 8 268 0
2023-03-05 62226 1 268 0
2023-03-04 62225 10 268 0
2023-03-03 62215 7 268 0
2023-03-02 62208 15 268 0
2023-03-01 62193 18 268 0
2023-02-28 62175 50 268 0
2023-02-27 62125 21 268 0
2023-02-26 62104 6 268 0
2023-02-25 62098 12 268 0
2023-02-24 62086 23 268 0
2023-02-23 62063 50 268 0
2023-02-22 62013 27 268 0
2023-02-21 61986 58 268 0
2023-02-20 61928 26 268 0
2023-02-19 61902 4 268 0
2023-02-18 61898 3 268 0
2023-02-17 61895 46 268 0
2023-02-16 61849 27 268 0
2023-02-15 61822 39 268 0
2023-02-14 61783 41 268 0
2023-02-13 61742 31 268 0
2023-02-12 61711 9 268 0
2023-02-11 61702 6 268 0
2023-02-10 61696 24 268 0
2023-02-09 61672 25 268 1
2023-02-08 61647 19 267 0
2023-02-07 61628 33 267 0
2023-02-06 61595 29 267 1
2023-02-05 61566 4 266 0
2023-02-04 61562 5 266 0
2023-02-03 61557 31 266 1
2023-02-02 61526 27 265 1
2023-02-01 61499 24 264 1
2023-01-31 61475 49 263 0
2023-01-30 61426 35 263 1
2023-01-29 61391 6 262 0
2023-01-28 61385 3 262 0
2023-01-27 61382 36 262 0
2023-01-26 61346 28 262 0
2023-01-25 61318 36 262 0
2023-01-24 61282 37 262 0
2023-01-23 61245 15 262 0
2023-01-22 61230 3 262 0
2023-01-21 61227 6 262 1
2023-01-20 61221 12 261 0
2023-01-19 61209 13 261 0
2023-01-18 61196 20 261 0
2023-01-17 61176 25 261 0
2023-01-16 61151 17 261 0
2023-01-15 61134 8 261 0
2023-01-14 61126 17 261 1
2023-01-13 61109 6 260 0
2023-01-12 61103 16 260 0
2023-01-11 61087 47 260 1
2023-01-10 61040 49 259 0
2023-01-09 60991 49 259 0
2023-01-08 60942 3 259 0
2023-01-07 60939 24 259 0
2023-01-06 60915 31 259 1
2023-01-05 60884 55 258 0
2023-01-04 60829 59 258 1
2023-01-03 60770 123 257 1
2023-01-02 60647 39 256 0
2023-01-01 60608 4 256 0
2022-12-31 60604 28 256 1
2022-12-30 60576 92 255 0
2022-12-29 60484 60 255 0
2022-12-28 60424 141 255 3
2022-12-27 60283 68 252 0
2022-12-26 60215 36 252 0
2022-12-25 60179 31 252 1
2022-12-24 60148 27 251 0
2022-12-23 60121 124 251 0
2022-12-22 59997 162 251 2
2022-12-21 59835 154 249 0
2022-12-20 59681 208 249 1
2022-12-19 59473 76 248 0
2022-12-18 59397 28 248 2
2022-12-17 59369 49 246 0
2022-12-16 59320 211 246 2
2022-12-15 59109 154 244 1
2022-12-14 58955 159 243 0
2022-12-13 58796 170 243 1
2022-12-12 58626 198 242 0
2022-12-11 58428 26 242 1
2022-12-10 58402 58 241 0
2022-12-09 58344 135 241 1
2022-12-08 58209 192 240 1
2022-12-07 58017 101 239 0
2022-12-06 57916 180 239 2
2022-12-05 57736 113 237 0
2022-12-04 57623 16 237 0
2022-12-03 57607 56 237 0
2022-12-02 57551 96 237 1
2022-12-01 57455 117 236 1
2022-11-30 57338 123 235 1
2022-11-29 57215 173 234 0
2022-11-28 57042 118 234 0
2022-11-27 56924 9 234 0
2022-11-26 56915 63 234 1
2022-11-25 56852 117 233 0
2022-11-24 56735 87 233 0
2022-11-23 56648 52 233 0
2022-11-22 56596 123 233 0
2022-11-21 56473 57 233 0
2022-11-20 56416 12 233 0
2022-11-19 56404 38 233 0
2022-11-18 56366 53 233 0
2022-11-17 56313 65 233 2
2022-11-16 56248 66 231 0
2022-11-15 56182 69 231 0
2022-11-14 56113 72 231 2
2022-11-13 56041 14 229 0
2022-11-12 56027 14 229 0
2022-11-11 56013 49 229 0
2022-11-10 55964 50 229 0
2022-11-09 55914 74 229 0
2022-11-08 55840 123 229 0
2022-11-07 55717 82 229 0
2022-11-06 55635 19 229 0
2022-11-05 55616 43 229 0
2022-11-04 55573 92 229 1
2022-11-03 55481 116 228 0
2022-11-02 55365 144 228 1
2022-11-01 55221 64 227 0
2022-10-31 55157 19 227 0
2022-10-30 55138 26 227 0
2022-10-29 55112 31 227 0
2022-10-28 55081 108 227 0
2022-10-27 54973 95 227 0
2022-10-26 54878 139 227 1
2022-10-25 54739 173 226 0
2022-10-24 54566 96 226 0
2022-10-23 54470 27 226 0
2022-10-22 54443 65 226 0
2022-10-21 54378 134 226 0
2022-10-20 54244 180 226 0
2022-10-19 54064 163 226 1
2022-10-18 53901 296 225 0
2022-10-17 53605 99 225 2
2022-10-16 53506 31 223 0
2022-10-15 53475 42 223 0
2022-10-14 53433 219 223 3
2022-10-13 53214 170 220 1
2022-10-12 53044 293 219 1
2022-10-11 52751 259 218 1
2022-10-10 52492 201 217 0
2022-10-09 52291 29 217 2
2022-10-08 52262 154 215 0
2022-10-07 52108 226 215 0
2022-10-06 51882 279 215 1
2022-10-05 51603 362 214 0
2022-10-04 51241 178 214 0
2022-10-03 51063 31 214 0
2022-10-02 51032 44 214 0
2022-10-01 50988 72 214 0
2022-09-30 50916 150 214 1
2022-09-29 50766 130 213 0
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2022-09-25 50147 31 212 0
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2022-09-22 49952 101 212 0
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2022-07-27 45992 256 205 1
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2021-01-04 1638 21 70 2
2021-01-02 1617 7 68 2
2020-12-31 1610 25 66 0
2020-12-30 1585 77 66 3
2020-12-29 1508 57 63 3
2020-12-28 1451 7 60 2
2020-12-27 1444 20 58 4
2020-12-25 1424 48 54 3
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2020-03-27 21 5 1 0
2020-03-26 16 6 1 1
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2020-03-18 2 1 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: 0:03:44.855750