2020-05-13: new data, formatting

koepferl · May 13, 2020 · 0206f07 · 0206f07
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diff --git a/DT_Bavaria.pdf b/DT_Bavaria.pdf
diff --git a/Documentation_COVID19_Local.ipynb b/Documentation_COVID19_Local.ipynb
@@ -8,7 +8,63 @@
  "source": [
  "# Local analysis of the case numbers of COVID19 for Germany\n",
  "\n",
- "#### Analyse der Corona Fallzahlen auf Landkreisebene in Deutschland (Deutsche Version folgt bald)"
+ "#### Analyse der Corona Fallzahlen auf Landkreisebene in Deutschland (Deutsche Version auf Nachbarseite)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## TODAY'S DATASET\n",
+ "\n",
+ "Description further down."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Dataset downloaded 2020-05-13\n",
+ "\n",
+ "* THERE IS NO GLORY IN PREVENTION BUT STILL LET'S TRY.\n",
+ "\n",
+ "\n",
+ "* DT evolution https://github.com/koepferl/COVID19Dahoam/blob/master/DT_Bavaria.pdf: \n",
+ " Changed y axis to log now. Most counties have now higher doubling times, isolation works. Stay put.\n",
+ " \n",
+ " * Bavaria with 187.17 d:\n",
+ " * 5 counties with lowest DTS (the larger the better):\n",
+ " * 40.16 12.5 LK Regen\n",
+ " * 50.97 02.5 SK Schweinfurt\n",
+ " * 51.72 23.4 SK Kempten\n",
+ " * 56.30 10.5 LK Neuburg-Schrobenhausen\n",
+ " * 61.56 07.5 SK Coburg\n",
+ " * 5 counties with highest DTs (the larger the better):\n",
+ " * 539.14 12.5 LK Roth\n",
+ " * 553.20 12.5 LK Weilheim-Schongau\n",
+ " * 617.51 09.5 LK Ostallgaeu\n",
+ " * 716.90 12.5 SK Wuezburg\n",
+ " * 936.20 12.5 LK Tirschenreuth\n",
+ "\n",
+ "* Semi-log plots https://github.com/koepferl/COVID19Dahoam/tree/master/plots: \n",
+ "\n",
+ " Counties with still very low DTs show also no flattening; with high DT almost a horizontal trend.\n",
+ " \n",
+ "* loglog plot https://github.com/koepferl/COVID19Dahoam/blob/master/loglog_Bavaria.pdf\n",
+ " Almost all counties move steep downwards (less infections every day) this means people are recovering faster than new once are becoming infected. This is good, our health system does not break down.\n",
+ "\n",
+ "* expert plots: In some counties now there a again more cases coming in, this causes the slope to steepen in the semilog plot, the DTs get shorter again and in the log-log plot the droping trend is revercing. In the expert plot we can see that also the reproduction time is goint up once the DT drops. The end of the loc-down is now visible."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "More interpretation follows tomorrow. \n",
+ "Find the interpretation history at the end of this page.\n",
+ "\n",
+ "## Recommendation\n",
+ "Social distancing is key to slow down the case numbers further to not overburden our local health system. Show solidarity through and from the distance. THERE IS NO GLORY IN PREVENTION BUT STILL LET'S TRY."
  ]
  },
  {
@@ -17,6 +73,8 @@
  "collapsed": true
  },
  "source": [
+ "## Documentation\n",
+ "\n",
  "Here the diagrams for the 96 Bavarian counties (more coming) of the evolution of the Corona cases are provided, analysed and the doubling times are given. The corresponding code to perform this analysis is provided as well. The plots are updated every day.\n",
  "\n",
  "#### Work for you - Mithilfe\n",
@@ -667,14 +725,22 @@
  "* loglog plot https://github.com/koepferl/COVID19Dahoam/blob/master/loglog_Bavaria.pdf\n",
  " Almost all counties move steep downwards (less infections every day) this means people are recovering faster than new once are becoming infected. This is good, our health system does not break down.\n",
  "\n",
- "* expert plots: In some counties now there a again more cases coming in, this causes the slope to steepen in the semilog plot, the DTs get shorter again and in the log-log plot the droping trend is revercing. In the expert plot we can see that also the reproduction time is goint up once the DT drops. The end of the loc-down is now visible.\n",
- "\n",
+ "* expert plots: In some counties now there a again more cases coming in, this causes the slope to steepen in the semilog plot, the DTs get shorter again and in the log-log plot the droping trend is revercing. In the expert plot we can see that also the reproduction time is goint up once the DT drops. The end of the loc-down is now visible."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
  "More interpretation follows tomorrow.\n",
  "\n",
  "\n",
  "\n",
  "## Recommendation\n",
  "Social distancing is key to slow down the case numbers further to not overburden our local health system. Show solidarity through and from the distance. THERE IS NO GLORY IN PREVENTION BUT STILL LET'S TRY.\n",
+ "\n",
  "\n"
  ]
  },

diff --git a/Documentation_COVID19_Local_German.ipynb b/Documentation_COVID19_Local_German.ipynb
@@ -10,12 +10,67 @@
  "#### Local analysis of the case numbers of COVID19 for Germany (English Version in other file)"
  ]
  },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### DATEN VON HEUTE (für Kenner alles auf einen Blick)\n",
+ "\n",
+ "Erklärung weiter unten."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Datendownload 2020-05-13\n",
+ "\n",
+ "\n",
+ "* Es gibt keine Dankbarkeit für Prävention, aber versuchen sollten wir es trotzdem.\n",
+ "\n",
+ "\n",
+ "* DT Entwicklung https://github.com/koepferl/COVID19Dahoam/blob/master/DT_Bavaria.pdf: \n",
+ " Nun ist die y-Achse auch logarithmisch. Die meisten Kreise haben höhere Verdopplungszeiten, Isolation funktioniert. Bleibt dahoam, auf Abstand oder tragt Masken.\n",
+ " * Bayern mit 187.17 Tage:\n",
+ " * 5 Kreise mit den niedriger Verdopplungszeiten (umso groesser desto besser):\n",
+ " * 40.16 12.5 LK Regen\n",
+ " * 50.97 02.5 SK Schweinfurt\n",
+ " * 51.72 23.4 SK Kempten\n",
+ " * 56.30 10.5 LK Neuburg-Schrobenhausen\n",
+ " * 61.56 07.5 SK Coburg\n",
+ " * 5 Kreise mit den hoechsten Verdopplungszeiten (umso groesser desto besser):\n",
+ " * 539.14 12.5 LK Roth\n",
+ " * 553.20 12.5 LK Weilheim-Schongau\n",
+ " * 617.51 09.5 LK Ostallgaeu\n",
+ " * 716.90 12.5 SK Wuezburg\n",
+ " * 936.20 12.5 LK Tirschenreuth\n",
+ "\n",
+ "* Semi-log plots https://github.com/koepferl/COVID19Dahoam/tree/master/plots: \n",
+ "\n",
+ " Kreise mit sehr niedrigen DTs werden nicht/kaum flacher; mit sehr großen DTs ist der Verlauf fast horizontal. \n",
+ " \n",
+ "* loglog plot https://github.com/koepferl/COVID19Dahoam/blob/master/loglog_Bavaria.pdf: \n",
+ "\n",
+ " Man sieht schon einen beginnenden Abwärtstrend für Kreise mit sehr hohen DTs. Das ist sehr gut, es bedeutet, dass weniger Menschen krank werden. Weniger Infektionen pro Tag bedeutet das die Anzahl der Genesenen bald stark zunimmt und unser Gesundheitssystem so nicht überlastet wird. \n",
+ "\n",
+ "* Expert plot: Viele Landkreise haben nun steigende tägliche Fallzahlen. Dies hat Auswirkungen auf die Steigung im Semi-log plot. Die Steigung wird wieder steiler, im DT plot wird die Verdopplungszeit dadurch wieder kürzer, im log-log plot wird der Abwärtstrend gebrochen und es geht wieder nach oben. Das Ende der Ausgangsperre ist nun auch in den Diagrammen angekommen. \n",
+ "\n",
+ "\n",
+ "\n",
+ "Mehr Diskussion folgt morgen. Die Historie der Diskussion finden Sie am Ende dieser Seite.\n",
+ "\n",
+ "## Handlungsempfehlung\n",
+ "Soziale Isolation in der Corona Kriese kann dabei helfen die Fallzahlenentwicklung abzubremsen um unser Gesundheitssystem nicht zu überfordern. Zeigen wir Solidarität durch Abstand. Es gibt keine Ehrung für Prävention, aber versuchen sollten wir es trotzdem!"
+ ]
+ },
  {
  "cell_type": "markdown",
  "metadata": {
  "collapsed": true
  },
  "source": [
+ "## Dokumentation\n",
+ "\n",
  "Hier werden täglich die Corona-Fallzahlen-Diagramme der 96 Bayerischen Land- und Stadtkreise angezeigt, analysiert. Zusätzlich werden auch die momentanen Verdopplungsraten diskutiert. Der zugehörige Analyse-Code ist ebenfalls hier enthalten. Die Diagramme werden täglich aktualisiert. \n",
  "\n",
  "#### Bitte um Mithilfe\n",

diff --git a/cov19_local.py b/cov19_local.py
@@ -916,7 +916,7 @@ def func(x, a, b):#, c):
 
  credit2 = 'Christine Greif\nhttp://www.usm.uni-muenchen.de/~koepferl\nThis work is licensed under CC-BY-SA 4.0\nData: NPGEO-DE'
 
- link = axs[2,3].text(x_pos, 0.1, credit2, fontsize=8, va = 'top') 
+ link = axs[2,3].text(x_pos, 0.5, credit2, fontsize=8, va = 'top') 
  link = axs3[2,3].text(x_pos, -2, credit2, fontsize=8)
  link = axs4[2,3].text(x_pos, -1., credit2, fontsize=8)
  link = axs2[2,3].text(3.5, 0.5, credit2, fontsize=8, va='top')
@@ -930,10 +930,14 @@ def func(x, a, b):#, c):
 
 
  for ax in axs.reshape(-1):
- ax.set_ylim(1.,500.9)
+ ax.set_ylim(1.5,500.9)
  ax.set_xlim(13,day_max)
 
+ from matplotlib.ticker import ScalarFormatter
+ for axis in [ax.xaxis, ax.yaxis]:
+ axis.set_major_formatter(ScalarFormatter())
  ax.grid(True, which="both")
+
  ax.set_xticks(np.arange(14, day_max, 4))
  ax.set_xticklabels([14, 18, 22, 26, 30, 3, 7, 11, 15, 19, 23, 27, 1, 5, 9, 13, 17])