Germany: LK Sächsische Schweiz-Osterzgebirge (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 15:20:27 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Sächsische Schweiz-Osterzgebirge", weeks=5);
2023-03-07T15:20:37.371716 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 45 45 50 50 7-day incidence rate (per 100K people) 45.5 LK Sächsische Schweiz-Osterzgebirge, 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 −0.050 −0.050 −0.025 −0.025 0.000 0.000 0.025 0.025 0.050 0.050 daily change Germany-LK Sächsische Schweiz-Osterzgebirge new deaths (rolling 7d mean) Germany-LK Sächsische Schweiz-Osterzgebirge 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-LK Sächsische Schweiz-Osterzgebirge cases daily growth factor Germany-LK Sächsische Schweiz-Osterzgebirge cases daily growth factor (rolling mean) Germany-LK Sächsische Schweiz-Osterzgebirge 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 Sächsische Schweiz-Osterzgebirge deaths daily growth factor Germany-LK Sächsische Schweiz-Osterzgebirge deaths daily growth factor (rolling mean) Germany-LK Sächsische Schweiz-Osterzgebirge estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2500 5000 7500 10000 cases doubling time [days] Germany-LK Sächsische Schweiz-Osterzgebirge doubling time cases (rolling mean) 0.0 12.2 24.4 36.6 daily change Germany-LK Sächsische Schweiz-Osterzgebirge new cases (rolling 7d mean) Germany-LK Sächsische Schweiz-Osterzgebirge new cases 0.000 0.245 0.489 0.734 0.979
In [4]:
overview(country="Germany", subregion="LK Sächsische Schweiz-Osterzgebirge");
2023-03-07T15:20:53.874301 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 7-day incidence rate (per 100K people) 45.5 LK Sächsische Schweiz-Osterzgebirge, 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 5 10 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 Sächsische Schweiz-Osterzgebirge cases daily growth factor Germany-LK Sächsische Schweiz-Osterzgebirge cases daily growth factor (rolling mean) Germany-LK Sächsische Schweiz-Osterzgebirge 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 Sächsische Schweiz-Osterzgebirge deaths daily growth factor Germany-LK Sächsische Schweiz-Osterzgebirge deaths daily growth factor (rolling mean) Germany-LK Sächsische Schweiz-Osterzgebirge 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 cases doubling time [days] Germany-LK Sächsische Schweiz-Osterzgebirge doubling time cases (rolling mean) Germany-LK Sächsische Schweiz-Osterzgebirge doubling time deaths (rolling mean) 0 488 976 1464 daily change Germany-LK Sächsische Schweiz-Osterzgebirge new cases (rolling 7d mean) Germany-LK Sächsische Schweiz-Osterzgebirge new cases 0.0 12.2 24.4 daily change Germany-LK Sächsische Schweiz-Osterzgebirge new deaths (rolling 7d mean) Germany-LK Sächsische Schweiz-Osterzgebirge new deaths 0 717 1435 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Sächsische Schweiz-Osterzgebirge", dates="2020-03-15:");
2023-03-07T15:22:56.563810 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 Sächsische Schweiz-Osterzgebirge LK Sächsische Schweiz-Osterzgebirge 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 Sächsische Schweiz-Osterzgebirge Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Sächsische Schweiz-Osterzgebirge")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Sächsische Schweiz-Osterzgebirge")
print(f'Population of country="Germany", subregion="LK Sächsische Schweiz-Osterzgebirge": {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 Sächsische Schweiz-Osterzgebirge": 244009 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 125258 28 1553 0
2023-03-03 125230 5 1553 0
2023-03-02 125225 19 1553 0
2023-03-01 125206 16 1553 0
2023-02-28 125190 43 1553 0
2023-02-27 125147 36 1553 0
2023-02-24 125111 18 1553 0
2023-02-23 125093 13 1553 0
2023-02-22 125080 21 1553 0
2023-02-21 125059 26 1553 0
2023-02-20 125033 30 1553 0
2023-02-17 125003 8 1553 0
2023-02-16 124995 16 1553 0
2023-02-15 124979 24 1553 0
2023-02-14 124955 23 1553 0
2023-02-13 124932 37 1553 0
2023-02-10 124895 11 1553 0
2023-02-09 124884 24 1553 0
2023-02-08 124860 17 1553 0
2023-02-07 124843 26 1553 0
2023-02-06 124817 25 1553 0
2023-02-03 124792 15 1553 0
2023-02-02 124777 19 1553 0
2023-02-01 124758 12 1553 0
2023-01-31 124746 34 1553 0
2023-01-30 124712 24 1553 0
2023-01-27 124688 17 1553 0
2023-01-26 124671 12 1553 0
2023-01-25 124659 15 1553 0
2023-01-24 124644 22 1553 0
2023-01-23 124622 19 1553 1
2023-01-20 124603 15 1552 0
2023-01-19 124588 10 1552 0
2023-01-18 124578 22 1552 0
2023-01-17 124556 29 1552 0
2023-01-16 124527 28 1552 0
2023-01-13 124499 6 1552 0
2023-01-12 124493 14 1552 0
2023-01-11 124479 23 1552 0
2023-01-10 124456 46 1552 0
2023-01-09 124410 51 1552 0
2023-01-06 124359 13 1552 0
2023-01-05 124346 41 1552 0
2023-01-04 124305 46 1552 0
2023-01-03 124259 74 1552 0
2023-01-02 124185 116 1552 0
2022-12-28 124069 48 1552 0
2022-12-27 124021 99 1552 0
2022-12-23 123922 28 1552 0
2022-12-22 123894 75 1552 0
2022-12-21 123819 49 1552 0
2022-12-20 123770 118 1552 0
2022-12-19 123652 127 1552 0
2022-12-16 123525 63 1552 3
2022-12-15 123462 79 1549 1
2022-12-14 123383 59 1548 0
2022-12-13 123324 120 1548 0
2022-12-12 123204 116 1548 1
2022-12-09 123088 101 1547 0
2022-12-08 122987 100 1547 0
2022-12-07 122887 88 1547 0
2022-12-06 122799 164 1547 2
2022-12-05 122635 110 1545 1
2022-12-02 122525 61 1544 0
2022-12-01 122464 92 1544 0
2022-11-30 122372 96 1544 1
2022-11-29 122276 112 1543 0
2022-11-28 122164 129 1543 1
2022-11-25 122035 67 1542 1
2022-11-24 121968 91 1541 0
2022-11-23 121877 72 1541 0
2022-11-22 121805 130 1541 0
2022-11-21 121675 135 1541 1
2022-11-18 121540 67 1540 0
2022-11-17 121473 104 1540 0
2022-11-15 121369 133 1540 0
2022-11-14 121236 111 1540 1
2022-11-11 121125 66 1539 0
2022-11-10 121059 66 1539 0
2022-11-09 120993 73 1539 0
2022-11-08 120920 157 1539 0
2022-11-07 120763 142 1539 1
2022-11-04 120621 100 1538 1
2022-11-03 120521 111 1537 1
2022-11-02 120410 166 1536 0
2022-11-01 120244 205 1536 2
2022-10-28 120039 108 1534 1
2022-10-27 119931 152 1533 0
2022-10-26 119779 181 1533 1
2022-10-25 119598 256 1532 1
2022-10-24 119342 284 1531 3
2022-10-22 119058 1 1528 0
2022-10-21 119057 146 1528 1
2022-10-20 118911 177 1527 0
2022-10-19 118734 240 1527 0
2022-10-18 118494 374 1527 2
2022-10-17 118120 370 1525 1
2022-10-14 117750 210 1524 1
2022-10-13 117540 272 1523 0
2022-10-12 117268 288 1523 1
2022-10-11 116980 332 1522 0
2022-10-10 116648 468 1522 6
2022-10-07 116180 285 1516 1
2022-10-06 115895 359 1515 1
2022-10-05 115536 403 1514 2
2022-10-04 115133 357 1512 1
2022-09-30 114776 149 1511 0
2022-09-29 114627 208 1511 0
2022-09-28 114419 198 1511 2
2022-09-27 114221 260 1509 2
2022-09-26 113961 192 1507 0
2022-09-23 113769 109 1507 0
2022-09-22 113660 135 1507 0
2022-09-21 113525 119 1507 0
2022-09-20 113406 219 1507 0
2022-09-19 113187 146 1507 0
2022-09-16 113041 122 1507 0
2022-09-15 112919 77 1507 0
2022-09-14 112842 80 1507 0
2022-09-13 112762 145 1507 0
2022-09-12 112617 152 1507 0
2022-09-09 112465 78 1507 0
2022-09-08 112387 83 1507 0
2022-09-07 112304 102 1507 0
2022-09-06 112202 89 1507 0
2022-09-05 112113 121 1507 0
2022-09-04 111992 1 1507 0
2022-09-02 111991 62 1507 0
2022-09-01 111929 75 1507 0
2022-08-31 111854 83 1507 1
2022-08-30 111771 124 1506 1
2022-08-29 111647 112 1505 0
2022-08-26 111535 85 1505 1
2022-08-25 111450 86 1504 0
2022-08-24 111364 90 1504 0
2022-08-23 111274 182 1504 0
2022-08-22 111092 167 1504 1
2022-08-19 110925 88 1503 0
2022-08-18 110837 115 1503 1
2022-08-17 110722 154 1502 2
2022-08-16 110568 200 1500 0
2022-08-15 110368 169 1500 0
2022-08-12 110199 89 1500 1
2022-08-11 110110 139 1499 0
2022-08-10 109971 112 1499 1
2022-08-09 109859 179 1498 0
2022-08-08 109680 211 1498 2
2022-08-05 109469 102 1496 0
2022-08-04 109367 117 1496 1
2022-08-03 109250 144 1495 0
2022-08-02 109106 263 1495 1
2022-08-01 108843 242 1494 1
2022-07-29 108601 98 1493 1
2022-07-28 108503 292 1492 2
2022-07-27 108211 259 1490 1
2022-07-26 107952 280 1489 0
2022-07-25 107672 168 1489 1
2022-07-22 107504 185 1488 1
2022-07-21 107319 222 1487 1
2022-07-20 107097 215 1486 3
2022-07-19 106882 361 1483 1
2022-07-18 106521 301 1482 1
2022-07-15 106220 236 1481 0
2022-07-14 105984 237 1481 0
2022-07-13 105747 104 1481 0
2022-07-12 105643 290 1481 3
2022-07-11 105353 234 1478 0
2022-07-08 105119 123 1478 0
2022-07-07 104996 179 1478 1
2022-07-06 104817 113 1477 0
2022-07-05 104704 210 1477 0
2022-07-04 104494 191 1477 1
2022-07-01 104303 89 1476 0
2022-06-30 104214 140 1476 0
2022-06-29 104074 96 1476 0
2022-06-28 103978 239 1476 1
2022-06-27 103739 198 1475 0
2022-06-24 103541 147 1475 2
2022-06-23 103394 144 1473 0
2022-06-22 103250 125 1473 0
2022-06-21 103125 162 1473 0
2022-06-20 102963 178 1473 0
2022-06-17 102785 74 1473 0
2022-06-16 102711 103 1473 0
2022-06-15 102608 85 1473 0
2022-06-14 102523 141 1473 1
2022-06-13 102382 135 1472 0
2022-06-10 102247 37 1472 0
2022-06-09 102210 84 1472 0
2022-06-08 102126 120 1472 0
2022-06-07 102006 111 1472 1
2022-06-03 101895 59 1471 1
2022-06-02 101836 49 1470 0
2022-06-01 101787 57 1470 0
2022-05-31 101730 90 1470 1
2022-05-30 101640 134 1469 0
2022-05-25 101506 53 1469 0
2022-05-24 101453 83 1469 0
2022-05-23 101370 89 1469 0
2022-05-20 101281 46 1469 1
2022-05-19 101235 65 1468 0
2022-05-18 101170 78 1468 1
2022-05-17 101092 111 1467 1
2022-05-16 100981 138 1466 0
2022-05-13 100843 59 1466 0
2022-05-12 100784 84 1466 0
2022-05-11 100700 99 1466 0
2022-05-10 100601 155 1466 2
2022-05-09 100446 176 1464 0
2022-05-06 100270 84 1464 0
2022-05-05 100186 102 1464 0
2022-05-04 100084 128 1464 0
2022-05-03 99956 216 1464 0
2022-05-02 99740 221 1464 2
2022-04-29 99519 139 1462 0
2022-04-28 99380 180 1462 0
2022-04-27 99200 199 1462 0
2022-04-26 99001 354 1462 1
2022-04-25 98647 388 1461 2
2022-04-22 98259 224 1459 0
2022-04-21 98035 295 1459 0
2022-04-20 97740 418 1459 3
2022-04-19 97322 896 1456 3
2022-04-14 96426 357 1453 1
2022-04-13 96069 308 1452 0
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2022-04-06 93620 495 1449 3
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2022-04-04 92324 698 1444 0
2022-04-03 91626 1 1444 0
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2022-03-31 91149 550 1443 4
2022-03-30 90599 754 1439 2
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2022-03-28 88874 1111 1434 0
2022-03-25 87763 698 1434 5
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2022-03-21 83293 1529 1420 0
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2022-03-16 79388 1232 1414 0
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2022-03-11 75479 783 1407 7
2022-03-10 74696 970 1400 3
2022-03-09 73726 841 1397 0
2022-03-08 72885 1187 1397 2
2022-03-07 71698 787 1395 5
2022-03-04 70911 456 1390 0
2022-03-03 70455 558 1390 0
2022-03-02 69897 681 1390 5
2022-03-01 69216 855 1385 2
2022-02-28 68361 736 1383 3
2022-02-26 67625 216 1380 0
2022-02-25 67409 360 1380 4
2022-02-24 67049 558 1376 2
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2022-02-22 65798 611 1373 1
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2022-02-17 63800 570 1370 1
2022-02-16 63230 456 1369 1
2022-02-15 62774 883 1368 2
2022-02-14 61891 598 1366 3
2022-02-12 61293 313 1363 0
2022-02-11 60980 276 1363 0
2022-02-10 60704 565 1363 1
2022-02-09 60139 666 1362 1
2022-02-08 59473 755 1361 1
2022-02-07 58718 313 1360 0
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2022-02-03 57701 465 1360 1
2022-02-02 57236 336 1359 0
2022-02-01 56900 584 1359 1
2022-01-31 56316 163 1358 2
2022-01-30 56153 2 1356 0
2022-01-29 56151 252 1356 0
2022-01-28 55899 195 1356 0
2022-01-27 55704 297 1356 1
2022-01-26 55407 220 1355 0
2022-01-25 55187 330 1355 0
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2022-01-13 53766 80 1349 1
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2021-11-10 28279 684 1085 6
2021-11-09 27595 626 1079 7
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2021-10-18 22710 165 1003 7
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2021-10-13 22392 98 995 3
2021-10-12 22294 155 992 2
2021-10-11 22139 91 990 0
2021-10-08 22048 56 990 0
2021-10-07 21992 74 990 1
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2021-10-05 21854 120 989 0
2021-10-04 21734 50 989 0
2021-10-01 21684 53 989 1
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2021-09-22 21348 29 987 0
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2021-09-16 21226 23 987 0
2021-09-15 21203 28 987 0
2021-09-14 21175 53 987 0
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2021-09-02 20957 11 987 0
2021-09-01 20946 23 987 0
2021-08-31 20923 24 987 0
2021-08-30 20899 11 987 0
2021-08-27 20888 13 987 0
2021-08-26 20875 15 987 0
2021-08-25 20860 3 987 0
2021-08-24 20857 18 987 0
2021-08-23 20839 16 987 0
2021-08-20 20823 7 987 0
2021-08-19 20816 13 987 0
2021-08-18 20803 6 987 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: 0:02:45.304113