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2020-04-21: new data, R number code
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koepferl committed Apr 21, 2020
1 parent 4a47e11 commit 9e9a77c
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27 changes: 27 additions & 0 deletions Documentation_COVID19_Local.ipynb
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Expand Up @@ -439,6 +439,33 @@
"* 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",
"### Dataset downloaded 2020-04-21\n",
"\n",
"* DT evolution https://github.com/koepferl/COVID19Dahoam/blob/master/DT_Bavaria.pdf: \n",
" Most counties have now higher doubling times, isolation works. Stay put.\n",
" \n",
" * Bavaria with 39.20 d:\n",
" * 5 counties with lowest DTS (the larger the better):\n",
" * 13.35 19.4 LK Neustadt a.d.Aisch-Bad Windsheim\n",
" * 13.47 18.4 LK Coburg\n",
" * 16.37 17.4 SK Amberg\n",
" * 17.24 18.4 SK Coburg\n",
" * 18.30 20.4 SK Straubing\n",
" * 5 counties with highest DTs (the larger the better):\n",
" * 80.64 20.4 LK Lindau\n",
" * 82.61 20.4 LK Landsberg a.Lech\n",
" * 93.78 20.4 LK Augsburg\n",
" * 98.49 19.4 LK Cham\n",
" * 104.10 19.4 LK Miesbach\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",
"\n",
"\n",
" \n",
"More interpretation follows tomorrow.\n",
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27 changes: 27 additions & 0 deletions Documentation_COVID19_Local_German.ipynb
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Expand Up @@ -424,6 +424,33 @@
"\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",
"### Datendownload 2020-04-21\n",
"\n",
"* DT Entwicklung https://github.com/koepferl/COVID19Dahoam/blob/master/DT_Bavaria.pdf: \n",
" Die meisten Kreise haben höhere Verdopplungszeiten, Isolation funktioniert. Bleibt dahoam, auf Abstand oder tragt Masken.\n",
" * Bayern mit 39.20 Tage:\n",
" * 5 Kreise mit den niedriger Verdopplungszeiten (umso groesser desto besser):\n",
" * 13.35 19.4 LK Neustadt a.d.Aisch-Bad Windsheim\n",
" * 13.47 18.4 LK Coburg\n",
" * 16.37 17.4 SK Amberg\n",
" * 17.24 18.4 SK Coburg\n",
" * 18.30 20.4 SK Straubing\n",
" * 5 Kreise mit den hoechsten Verdopplungszeiten (umso groesser desto besser):\n",
" * 80.64 20.4 LK Lindau\n",
" * 82.61 20.4 LK Landsberg a.Lech\n",
" * 93.78 20.4 LK Augsburg\n",
" * 98.49 19.4 LK Cham\n",
" * 104.10 19.4 LK Miesbach\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",
" \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",
"\n",
"\n",
"\n",
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43 changes: 41 additions & 2 deletions cov19_local.py
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Expand Up @@ -250,6 +250,7 @@ def func(x, a, b):#, c):
DTs = []
Ntot_today = []
Ntot_week = []
R4s = []

col = 30
colapp = []
Expand Down Expand Up @@ -283,6 +284,7 @@ def func(x, a, b):#, c):
#print num_diff_8

R4 = np.sum(num_diff_8[4:]) / np.sum(num_diff_8[0:4])
R4s.append(R4)
#print num_diff_8[0:4], num_diff_8[4:]
#########
# doubling time
Expand Down Expand Up @@ -415,7 +417,7 @@ def func(x, a, b):#, c):
'ill_rate': (num - num_gesund - num_tod) / num * 100,
'day': day}

return [name, day[7:], DTs, Ntot_today, Ntot_week, rates]
return [name, day[7:], DTs, Ntot_today, Ntot_week, rates, R4s]

#####################################
# Doubeling Time (Verdopplungszeit) #
Expand Down Expand Up @@ -507,6 +509,7 @@ def func(x, a, b):#, c):
fig, axs = plt.subplots(nrow, ncol, figsize=(28,21))
fig2, axs2 = plt.subplots(nrow, ncol, figsize=(28,21))
fig3, axs3 = plt.subplots(nrow, ncol, figsize=(28,21))
fig4, axs4 = plt.subplots(nrow, ncol, figsize=(28,21))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.1, hspace=0.1)
axs[0,0].set_title('Entwicklung der Verdopplungszeiten auf Kreisebene')
axs[0,1].set_title('Evolution of the Doubeling Time for German Counties')
Expand Down Expand Up @@ -547,72 +550,84 @@ def func(x, a, b):#, c):
ax = axs[0,0]
ax2 = axs2[0,0]
ax3 = axs3[0,0]
ax4 = axs4[0,0]
line_col = 20 + 30 * (8 - i)

if i in range(8, 16):
ax = axs[0,1]
ax2 = axs2[0,1]
ax3 = axs3[0,1]
ax4 = axs4[0,1]
line_col = 20 + 30 * (16 - i)

if i in range(16,24):
ax = axs[0,2]
ax2 = axs2[0,2]
ax3 = axs3[0,2]
ax4 = axs4[0,2]
line_col = 20 + 30 * (24 - i)

if i in range(24,32):
ax = axs[0,3]
ax2 = axs2[0,3]
ax3 = axs3[0,3]
ax4 = axs4[0,3]
line_col = 20 + 30 * (32 - i)

if i in range(32,40):
ax = axs[1,0]
ax2 = axs2[1,0]
ax3 = axs3[1,0]
ax4 = axs4[1,0]
line_col = 20 + 30 * (40 - i)

if i in range(40,48):
ax = axs[1,1]
ax2 = axs2[1,1]
ax3 = axs3[1,1]
ax4 = axs4[1,1]
line_col = 20 + 30 * (48 - i)

if i in range(48,56):
ax = axs[1,2]
ax2 = axs2[1,2]
ax3 = axs3[1,2]
ax4 = axs4[1,2]
line_col = 20 + 30 * (56 - i)

if i in range(56,64):
ax = axs[1,3]
ax2 = axs2[1,3]
ax3 = axs3[1,3]
ax4 = axs4[1,3]
line_col = 20 + 30 * (64 - i)

if i in range(64,72):
ax = axs[2,0]
ax2 = axs2[2,0]
ax3 = axs3[2,0]
ax4 = axs4[2,0]
line_col = 20 + 30 * (72 - i)

if i in range(72,80):
ax = axs[2,1]
ax2 = axs2[2,1]
ax3 = axs3[2,1]
ax4 = axs4[2,1]
line_col = 20 + 30 * (80 - i)

if i in range(80,88):
ax = axs[2,2]
ax2 = axs2[2,2]
ax3 = axs3[2,2]
ax4 = axs4[2,2]
line_col = 20 + 30 * (88 - i)

if i in range(88,96):
ax = axs[2,3]
ax2 = axs2[2,3]
ax3 = axs3[2,3]
ax4 = axs4[2,3]
line_col = 20 + 30 * (96 - i)

key = sorted_keys[i]
Expand All @@ -627,6 +642,7 @@ def func(x, a, b):#, c):
print DT[key][2][-1], int(DT[key][1][-1]), DT[key][0]

ax3.plot(DT[key][5]['day'], DT[key][5]['death_rate'], '*-', c = cmap(line_col), label=DT[key][0])
ax4.plot(DT[key][1], DT[key][6], '.-', c = cmap(line_col), label=DT[key][0])
#ax3.plot(DT[key][5]['day'], DT[key][5]['recover_rate'], 'o-', c = cmap(line_col), label=DT[key][0])
#ax3.plot(DT[key][5]['day'], DT[key][5]['ill_rate'], 'x-', c = cmap(line_col), label=DT[key][0])

Expand All @@ -642,6 +658,10 @@ def func(x, a, b):#, c):
axs3[2,3].text(27, -8, 'This work is licensed under CC-BY-SA 4.0', fontsize=8)
axs3[2,3].text(27, -9, 'Data: NPGEO-DE', fontsize=8)

link = axs4[2,3].text(27, -1., 'Christine Greif (http://www.usm.uni-muenchen.de/~koepferl)', fontsize=8)
axs4[2,3].text(27, -1.5, 'This work is licensed under CC-BY-SA 4.0', fontsize=8)
axs4[2,3].text(27, -2., 'Data: NPGEO-DE', fontsize=8)

link = axs2[2,3].text(35, 5, 'Christine Greif (http://www.usm.uni-muenchen.de/~koepferl)', fontsize=8)
axs2[2,3].text(35, 4.4, 'This work is licensed under CC-BY-SA 4.0', fontsize=8)
axs2[2,3].text(35, 3.8, 'Data: NPGEO-DE', fontsize=8)
Expand Down Expand Up @@ -692,7 +712,8 @@ def func(x, a, b):#, c):
ax3.grid(True, which="both")
ax3.set_xticks(np.arange(14, 60, 2))
ax3.set_xticklabels([14, 16, 18, 20, 22, 24, 26, 28, 30, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27])
ax3.set_ylabel('Sterberaten in %')
if ax3 in [axs3[0,0], axs3[1,0], axs3[2,0]]:
ax3.set_ylabel('Sterberaten in %')


ax3.legend(loc='upper left')
Expand All @@ -702,11 +723,29 @@ def func(x, a, b):#, c):
ax3.text(31, -6, 'April')


for ax4 in axs4.reshape(-1):
ax4.set_ylim(0,4.9)
ax4.set_xlim(13,60)

ax4.grid(True, which="both")
ax4.set_xticks(np.arange(14, 60, 2))
ax4.set_xticklabels([14, 16, 18, 20, 22, 24, 26, 28, 30, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27])

ax4.legend(loc='upper left')

#if ax in [axs[2,0], axs[2,1], axs[2,2], axs[2,3]]:
ax4.text(13, -0.5, 'Maerz/March')
ax4.text(31, -0.5, 'April')

if ax4 in [axs4[1,0]]:
ax4.set_ylabel('geschaetzte Reproduktionszahl R (Anzahl letzten 4 Meldungen / Anzahl der letzten 4 Meldungen davor)')


#plt.show()
fig.savefig('DT_' + state[2] + '.pdf', dpi=300, overwrite=True, bbox_inches='tight')
fig2.savefig('loglog_' + state[2] + '.pdf', dpi=300, overwrite=True, bbox_inches='tight')
fig3.savefig('rate_' + state[2] + '.pdf', dpi=300, overwrite=True, bbox_inches='tight')
fig4.savefig('R_' + state[2] + '.pdf', dpi=300, overwrite=True, bbox_inches='tight')

def docu(LK_ID, DT):
print '*' * 30
Expand Down
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