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2020-04-23: new data, small formatting
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@koepferl
koepferl committed Apr 23, 2020
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30 changes: 30 additions & 0 deletions Documentation_COVID19_Local.ipynb
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Expand Up @@ -465,6 +465,36 @@
"* 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 2020-04-23\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 50.65 d:\n",
" * 5 counties with lowest DTS (the larger the better):\n",
" * 14.59 21.4 LK Neustadt a.d.Aisch-Bad Windsheim\n",
" * 21.54 21.4 LK Kitzingen\n",
" * 21.72 21.4 SK Coburg\n",
" * 21.82 22.4 LK Kronach\n",
" * 22.22 21.4 LK Neumarkt i.d.OPf.\n",
" * 5 counties with highest DTs (the larger the better):\n",
" * 98.77 19.4 LK Cham\n",
" * 99.51 22.4 LK Freising\n",
" * 109.27 21.4 LK Weilheim-Schongau\n",
" * 120.56 21.4 LK Miesbach\n",
" * 131.96 22.4 LK Starnberg\n",
"\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",
" Quite interesting. For some counties there are small \"easter dents\" roughly one week after easter. Apparently the disciplin was not as high around easter. I am worried about now, were even more is possible. I hope it does not get too bad, although I am expecting it - actually.\n",
"\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",
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29 changes: 29 additions & 0 deletions Documentation_COVID19_Local_German.ipynb
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Expand Up @@ -451,6 +451,35 @@
"\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 2020-04-23\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 50.65 Tage:\n",
" * 5 Kreise mit den niedriger Verdopplungszeiten (umso groesser desto besser):\n",
" * 14.59 21.4 LK Neustadt a.d.Aisch-Bad Windsheim\n",
" * 21.54 21.4 LK Kitzingen\n",
" * 21.72 21.4 SK Coburg\n",
" * 21.82 22.4 LK Kronach\n",
" * 22.22 21.4 LK Neumarkt i.d.OPf.\n",
" * 5 Kreise mit den hoechsten Verdopplungszeiten (umso groesser desto besser):\n",
" * 98.77 19.4 LK Cham\n",
" * 99.51 22.4 LK Freising\n",
" * 109.27 21.4 LK Weilheim-Schongau\n",
" * 120.56 21.4 LK Miesbach\n",
" * 131.96 22.4 LK Starnberg\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",
" Ganz interessant, bei einigen Landkreisen gibt es einen kleinen “Osterhaken\" ungefähr eine Woche nach Ostern. Da war die Disziplin wohl nicht ganz so strickt. Ich hoffe des wird jetzt (wo noch mehr erlaubt) nicht wieder zu schlimm. Obwohl ich es eigentlich erwarte.\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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24 changes: 20 additions & 4 deletions cov19_local.py
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Expand Up @@ -274,9 +274,25 @@ def func(x, a, b):#, c):
Ntot_today.append(num[cut-8:cut][-1])
Ntot_week.append(num_week[-1] - num_week[0])

########
# Reproduction number for Rtime notification days (4 days strict)

#%%%%%%%%#daytoday = int(day[cut-1])
#%%%%%%%%#dayminus4 = int(daytoday - 5)
#%%%%%%%%#dayminus8 = int(daytoday - 9)
#%%%%%%%%#print daytoday, dayminus4, dayminus8
#%%%%%%%%#
#%%%%%%%%#print num[day == daytoday], num[day == dayminus4], num[day == dayminus8]
#%%%%%%%%#
#%%%%%%%%#R4_row = (num[day == daytoday] - num[day == dayminus4]) / (num[day == dayminus4] - num[day == dayminus8])
#%%%%%%%%#print R4_row
#%%%%%%%%#
#%%%%%%%%#raise Exception('stop')

########
# Reproduction number for Rtime notification days
#print num[cut-8:cut]

if cut-9 < 0:
num_before_int = num[cut-8:cut][0]
else: num_before_int = num[cut-8] - num[cut-9]
Expand All @@ -294,7 +310,7 @@ def func(x, a, b):#, c):
DTs.append(DT)
#print popt, np.log(2) / popt[1]

print '%02d'%int(day_real[cut-1]) + '.' + '%02d'%int(month[cut-1]), '%5.2f'%DT, '%5.2f'%R4
print '%02d'%int(day_real[cut-1]) + '.' + '%02d'%int(month[cut-1]), '%6.2f'%DT, '%6.2f'%R4

#print("a =", popt[0], "+/-", pcov[0,0]**0.5)
#print("b =", popt[1], "+/-", pcov[1,1]**0.5)
Expand All @@ -308,7 +324,7 @@ def func(x, a, b):#, c):
########
# plot fit
#########
day_label = 'Fit am ' + '%02d'%int(day_real[cut-1]) + '.' + '%02d'%int(month[cut-1]) + '; VZ: ' + '%5.2f'%DT + ' d'
day_label = 'Fit am ' + '%02d'%int(day_real[cut-1]) + '.' + '%02d'%int(month[cut-1]) + '; VZ: ' + '%6.2f'%DT + ' d'
plt.semilogy(x, np.exp(func(x, *popt)), '-', color=plt.cm.viridis(int(col)),
label=day_label)

Expand Down Expand Up @@ -778,7 +794,7 @@ def docu(LK_ID, DT):
print ' * 5 counties with highest DTs (the larger the better):'

if (i < 5) or (i > len(name_print) - 6) :
print ' *', '%5.2f'%DT_print[i], str(int(day_print[i]-31)) + '.4', name_print[i]
print ' *', '%6.2f'%DT_print[i], str(int(day_print[i]-31)) + '.4', name_print[i]


print '*' * 30
Expand All @@ -794,5 +810,5 @@ def docu(LK_ID, DT):
print ' * 5 Kreise mit den hoechsten Verdopplungszeiten (umso groesser desto besser):'

if (i < 5) or (i > len(name_print) - 6) :
print ' *', '%5.2f'%DT_print[i], str(int(day_print[i]-31)) + '.4', name_print[i]
print ' *', '%6.2f'%DT_print[i], str(int(day_print[i]-31)) + '.4', name_print[i]

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