Germany: SK Rostock (Mecklenburg-Vorpommern)¶

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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 12:58:55 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="SK Rostock", weeks=5);
2023-03-07T12:59:06.040003 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 50 50 60 60 70 70 7-day incidence rate (per 100K people) 59.0 SK Rostock, 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 20 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-SK Rostock new deaths (rolling 7d mean) Germany-SK Rostock new deaths 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-SK Rostock cases daily growth factor Germany-SK Rostock cases daily growth factor (rolling mean) Germany-SK Rostock 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-SK Rostock deaths daily growth factor Germany-SK Rostock deaths daily growth factor (rolling mean) Germany-SK Rostock estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] Germany-SK Rostock doubling time cases (rolling mean) 0.00 10.42 20.84 31.26 41.68 daily change Germany-SK Rostock new cases (rolling 7d mean) Germany-SK Rostock new cases 0.000 0.324 0.648 0.973
In [4]:
overview(country="Germany", subregion="SK Rostock");
2023-03-07T12:59:25.003608 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 3000 3000 7-day incidence rate (per 100K people) 59.0 SK Rostock, 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 0 200 400 600 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 0.5 1.0 1.5 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-SK Rostock cases daily growth factor Germany-SK Rostock cases daily growth factor (rolling mean) Germany-SK Rostock 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-SK Rostock deaths daily growth factor Germany-SK Rostock deaths daily growth factor (rolling mean) Germany-SK Rostock 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 cases doubling time [days] Germany-SK Rostock doubling time cases (rolling mean) Germany-SK Rostock doubling time deaths (rolling mean) 0 417 834 1250 daily change Germany-SK Rostock new cases (rolling 7d mean) Germany-SK Rostock new cases 0.000 1.042 2.084 3.126 daily change Germany-SK Rostock new deaths (rolling 7d mean) Germany-SK Rostock new deaths 0.0 49.1 98.2 147.2 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="SK Rostock", dates="2020-03-15:");
2023-03-07T13:01:58.765276 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 Rostock SK Rostock 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 Rostock Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="SK Rostock")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="SK Rostock")
print(f'Population of country="Germany", subregion="SK Rostock": {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 Rostock": 208400 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 94413 22 186 0
2023-03-03 94391 11 186 0
2023-03-02 94380 26 186 0
2023-03-01 94354 21 186 0
2023-02-28 94333 43 186 0
2023-02-27 94290 16 186 0
2023-02-24 94274 22 186 0
2023-02-23 94252 16 186 0
2023-02-22 94236 12 186 0
2023-02-21 94224 39 186 0
2023-02-20 94185 25 186 0
2023-02-17 94160 23 186 0
2023-02-16 94137 23 186 0
2023-02-15 94114 25 186 0
2023-02-14 94089 25 186 0
2023-02-13 94064 14 186 0
2023-02-10 94050 17 186 0
2023-02-09 94033 23 186 0
2023-02-08 94010 31 186 0
2023-02-07 93979 37 186 0
2023-02-06 93942 40 186 0
2023-02-03 93902 32 186 0
2023-02-02 93870 21 186 0
2023-02-01 93849 25 186 0
2023-01-31 93824 45 186 0
2023-01-30 93779 35 186 0
2023-01-27 93744 11 186 0
2023-01-26 93733 21 186 0
2023-01-25 93712 16 186 0
2023-01-24 93696 38 186 0
2023-01-23 93658 25 186 0
2023-01-20 93633 23 186 0
2023-01-19 93610 26 186 0
2023-01-18 93584 31 186 0
2023-01-17 93553 36 186 0
2023-01-16 93517 38 186 0
2023-01-13 93479 27 186 0
2023-01-12 93452 26 186 0
2023-01-11 93426 27 186 0
2023-01-10 93399 83 186 0
2023-01-09 93316 55 186 0
2023-01-06 93261 51 186 0
2023-01-05 93210 58 186 1
2023-01-04 93152 62 185 0
2023-01-03 93090 129 185 0
2023-01-02 92961 97 185 1
2022-12-30 92864 65 184 0
2022-12-29 92799 67 184 0
2022-12-28 92732 144 184 1
2022-12-27 92588 180 183 0
2022-12-23 92408 99 183 0
2022-12-22 92309 119 183 0
2022-12-21 92190 120 183 1
2022-12-20 92070 189 182 0
2022-12-19 91881 163 182 0
2022-12-16 91718 112 182 0
2022-12-15 91606 134 182 0
2022-12-14 91472 171 182 1
2022-12-13 91301 240 181 0
2022-12-12 91061 154 181 0
2022-12-09 90907 110 181 0
2022-12-08 90797 153 181 0
2022-12-07 90644 129 181 0
2022-12-06 90515 242 181 0
2022-12-05 90273 197 181 0
2022-12-02 90076 102 181 0
2022-12-01 89974 137 181 1
2022-11-30 89837 162 180 0
2022-11-29 89675 203 180 0
2022-11-28 89472 158 180 0
2022-11-25 89314 114 180 0
2022-11-24 89200 71 180 0
2022-11-23 89129 138 180 0
2022-11-22 88991 164 180 0
2022-11-21 88827 150 180 0
2022-11-18 88677 85 180 0
2022-11-17 88592 87 180 0
2022-11-16 88505 118 180 1
2022-11-15 88387 182 179 1
2022-11-14 88205 148 178 0
2022-11-11 88057 95 178 0
2022-11-10 87962 118 178 0
2022-11-09 87844 135 178 1
2022-11-08 87709 190 177 0
2022-11-07 87519 209 177 0
2022-11-04 87310 148 177 0
2022-11-03 87162 195 177 0
2022-11-02 86967 215 177 0
2022-11-01 86752 367 177 0
2022-10-28 86385 169 177 0
2022-10-27 86216 233 177 0
2022-10-26 85983 191 177 0
2022-10-25 85792 335 177 0
2022-10-24 85457 265 177 0
2022-10-21 85192 218 177 0
2022-10-20 84974 218 177 0
2022-10-19 84756 262 177 0
2022-10-18 84494 358 177 0
2022-10-17 84136 296 177 0
2022-10-14 83840 215 177 0
2022-10-13 83625 169 177 0
2022-10-12 83456 239 177 0
2022-10-11 83217 325 177 1
2022-10-10 82892 333 176 0
2022-10-07 82559 297 176 0
2022-10-06 82262 278 176 1
2022-10-05 81984 397 175 0
2022-10-04 81587 354 175 0
2022-09-30 81233 147 175 0
2022-09-29 81086 175 175 1
2022-09-28 80911 232 174 0
2022-09-27 80679 243 174 0
2022-09-26 80436 227 174 1
2022-09-23 80209 121 173 0
2022-09-22 80088 138 173 0
2022-09-21 79950 158 173 0
2022-09-20 79792 211 173 1
2022-09-19 79581 182 172 0
2022-09-16 79399 107 172 0
2022-09-15 79292 162 172 0
2022-09-14 79130 90 172 0
2022-09-13 79040 158 172 0
2022-09-12 78882 177 172 1
2022-09-09 78705 105 171 0
2022-09-08 78600 94 171 0
2022-09-07 78506 122 171 0
2022-09-06 78384 164 171 0
2022-09-05 78220 142 171 0
2022-09-02 78078 98 171 0
2022-09-01 77980 55 171 0
2022-08-31 77925 145 171 0
2022-08-30 77780 120 171 0
2022-08-29 77660 33 171 0
2022-08-28 77627 30 171 0
2022-08-27 77597 31 171 1
2022-08-26 77566 164 170 0
2022-08-25 77402 95 170 0
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2022-08-22 77060 158 169 2
2022-08-19 76902 133 167 0
2022-08-18 76769 116 167 0
2022-08-17 76653 127 167 1
2022-08-16 76526 260 166 0
2022-08-15 76266 187 166 1
2022-08-12 76079 149 165 0
2022-08-11 75930 133 165 0
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2022-08-09 75611 213 165 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: 0:03:57.267887