Germany: LK Neuwied (Rheinland-Pfalz)¶

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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 14:56:19 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Neuwied", weeks=5);
2023-03-07T14:56:36.276797 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 150 150 200 200 250 250 7-day incidence rate (per 100K people) 106.3 LK Neuwied, 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 60 80 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.00 0.05 0.05 0.10 0.10 daily change Germany-LK Neuwied new deaths (rolling 7d mean) Germany-LK Neuwied new deaths 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Neuwied cases daily growth factor Germany-LK Neuwied cases daily growth factor (rolling mean) Germany-LK Neuwied estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Neuwied deaths daily growth factor Germany-LK Neuwied deaths daily growth factor (rolling mean) Germany-LK Neuwied estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Neuwied doubling time cases (rolling mean) 0.0 36.9 73.8 110.6 147.5 daily change Germany-LK Neuwied new cases (rolling 7d mean) Germany-LK Neuwied new cases 0.000 0.314 0.628 0.942
In [4]:
overview(country="Germany", subregion="LK Neuwied");
2023-03-07T14:56:53.077574 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 7-day incidence rate (per 100K people) 106.3 LK Neuwied, 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 4 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 R & growth factor (based on cases) Germany-LK Neuwied cases daily growth factor Germany-LK Neuwied cases daily growth factor (rolling mean) Germany-LK Neuwied estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on deaths) Germany-LK Neuwied deaths daily growth factor Germany-LK Neuwied deaths daily growth factor (rolling mean) Germany-LK Neuwied 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 Neuwied doubling time cases (rolling mean) Germany-LK Neuwied doubling time deaths (rolling mean) 0.0 184.4 368.8 553.2 737.6 daily change Germany-LK Neuwied new cases (rolling 7d mean) Germany-LK Neuwied new cases 0.000 1.844 3.688 5.532 7.376 daily change Germany-LK Neuwied new deaths (rolling 7d mean) Germany-LK Neuwied new deaths 0.0 47.5 95.0 142.4 189.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Neuwied", dates="2020-03-15:");
2023-03-07T14:59:26.712866 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 Neuwied LK Neuwied 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 Neuwied Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Neuwied")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Neuwied")
print(f'Population of country="Germany", subregion="LK Neuwied": {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 Neuwied": 184390 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 68590 15 298 0
2023-03-03 68575 17 298 0
2023-03-02 68558 32 298 0
2023-03-01 68526 43 298 0
2023-02-28 68483 89 298 0
2023-02-27 68394 171 298 0
2023-02-24 68223 77 298 0
2023-02-23 68146 81 298 0
2023-02-22 68065 48 298 0
2023-02-21 68017 43 298 0
2023-02-20 67974 59 298 0
2023-02-17 67915 11 298 0
2023-02-16 67904 31 298 0
2023-02-15 67873 33 298 0
2023-02-14 67840 42 298 0
2023-02-13 67798 47 298 0
2023-02-10 67751 26 298 0
2023-02-09 67725 21 298 0
2023-02-08 67704 30 298 0
2023-02-07 67674 47 298 0
2023-02-06 67627 54 298 0
2023-02-03 67573 22 298 0
2023-02-02 67551 30 298 0
2023-02-01 67521 30 298 0
2023-01-31 67491 34 298 0
2023-01-30 67457 34 298 1
2023-01-27 67423 17 297 0
2023-01-26 67406 21 297 0
2023-01-25 67385 22 297 0
2023-01-24 67363 29 297 0
2023-01-23 67334 41 297 0
2023-01-20 67293 32 297 0
2023-01-19 67261 14 297 0
2023-01-18 67247 22 297 0
2023-01-17 67225 31 297 0
2023-01-16 67194 50 297 0
2023-01-13 67144 14 297 0
2023-01-12 67130 11 297 0
2023-01-11 67119 33 297 0
2023-01-10 67086 19 297 0
2023-01-09 67067 67 297 1
2023-01-06 67000 9 296 0
2023-01-05 66991 28 296 1
2023-01-04 66963 51 295 0
2023-01-03 66912 42 295 0
2023-01-02 66870 92 295 1
2022-12-30 66778 25 294 0
2022-12-29 66753 64 294 0
2022-12-28 66689 74 294 2
2022-12-27 66615 160 292 1
2022-12-23 66455 29 291 1
2022-12-22 66426 81 290 0
2022-12-21 66345 74 290 0
2022-12-20 66271 64 290 1
2022-12-19 66207 114 289 3
2022-12-16 66093 37 286 0
2022-12-15 66056 55 286 0
2022-12-14 66001 43 286 0
2022-12-13 65958 66 286 0
2022-12-12 65892 112 286 0
2022-12-09 65780 55 286 0
2022-12-08 65725 57 286 0
2022-12-07 65668 31 286 0
2022-12-06 65637 62 286 0
2022-12-05 65575 82 286 1
2022-12-02 65493 49 285 0
2022-12-01 65444 42 285 0
2022-11-30 65402 51 285 0
2022-11-29 65351 43 285 0
2022-11-28 65308 75 285 1
2022-11-25 65233 38 284 0
2022-11-24 65195 47 284 0
2022-11-23 65148 53 284 0
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2022-11-15 64753 58 284 1
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2022-10-31 63933 156 283 0
2022-10-28 63777 88 283 0
2022-10-27 63689 90 283 1
2022-10-26 63599 130 282 1
2022-10-25 63469 167 281 0
2022-10-24 63302 254 281 1
2022-10-21 63048 137 280 0
2022-10-20 62911 124 280 0
2022-10-19 62787 195 280 0
2022-10-18 62592 226 280 0
2022-10-17 62366 416 280 1
2022-10-14 61950 158 279 0
2022-10-13 61792 184 279 0
2022-10-12 61608 205 279 0
2022-10-11 61403 313 279 0
2022-10-10 61090 476 279 0
2022-10-07 60614 260 279 0
2022-10-06 60354 348 279 0
2022-10-05 60006 382 279 0
2022-10-04 59624 453 279 1
2022-09-30 59171 149 278 1
2022-09-29 59022 197 277 0
2022-09-28 58825 149 277 0
2022-09-27 58676 192 277 0
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2022-09-20 57757 111 277 0
2022-09-19 57646 172 277 0
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2022-09-15 57373 91 277 1
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2022-09-06 56740 100 276 0
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2022-08-12 55418 51 275 0
2022-08-11 55367 80 275 0
2022-08-10 55287 70 275 0
2022-08-09 55217 91 275 0
2022-08-08 55126 142 275 1
2022-08-05 54984 56 274 0
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2020-12-29 3516 31 104 2
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2020-12-17 2913 91 76 2
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2020-12-15 2760 26 74 5
2020-12-14 2734 62 69 2
2020-12-12 2672 120 67 7
2020-12-11 2552 81 60 3
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2020-12-09 2379 82 48 1
2020-12-08 2297 23 47 0
2020-12-07 2274 15 47 0
2020-12-05 2259 50 47 2
2020-12-04 2209 112 45 4
2020-12-03 2097 79 41 6
2020-12-02 2018 31 35 1
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2020-11-28 1951 44 33 3
2020-11-27 1907 77 30 3
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2020-11-25 1803 37 27 3
2020-11-24 1766 8 24 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: 0:03:21.945008